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A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks
Computer Communications 137 (2019) 15–31
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A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks Da-Ren Chen a ,∗, Lin-Chih Chen b , Mu-Yen Chen a , Ming-Yang Hsu a a b
Department of Information Management, National Taichung University of Science and Technology, Taichung, Taiwan, ROC Department of Information Management, National Dong Hwa University, Hualien, Taiwan, ROC
ARTICLE
INFO
Keywords: Wireless sensor network Residual energy Dijkstra shortest path algorithm Minimum spanning tree Borůvka algorithm Distributed algorithm Self-stabilizing algorithm
ABSTRACT Wireless sensor network (WSNs) are composed of a large number of battery-powered wireless sensors, which acquire and monitor physical data from their surroundings through self-organization. The sensors are deployed randomly in a target area where maintenance and battery replacement are difficult or even impossible. To achieve better coverage and prolong network lifetime, networks typically adopt clustering protocols with hierarchical inter-cluster topology for network management and data acquisition in WSNs. However, typical solutions require cluster re-configuration due to early death of cluster heads (CHs) and cause energy inefficiency. This paper proposes a coverage- and energy-aware protocol with intra- and inter-cluster methods called CEMST that considers the sensor node density and coverage overlapping. In addition, to adapt network dynamics while improving energy efficiency, self-stabilizing algorithm and Borůvka algorithm are applied to construct the minimum spanning trees (MST) for intra- and inter-cluster routes, respectively. Simulation results indicate that CEMST produces the balanced clustering structures and provides better coverage and longer network lifetime than previous methods.
1. Introduction The development of tiny, inexpensive and versatile sensors with wireless transceivers has been driven by the increasing use of wireless sensor network (WSN) applications. A WSN consists of a number of sensor nodes capable of data acquisition, processing and transmission. WSNs have been extensively implemented in industrial, civilian and military applications [1–7]. Due to bandwidth and energy constraints, the sensor nodes cannot communicate directly with other sensors over long distances. A multihop protocol is needed to transmit data from a sensor to a base-station out of the sensor’s transmission range. Cluster-based methods [8–18] provide better energy efficiency than multi-hop protocol by using data fusion within a cluster head (CH), thus decreasing data transmission and improving network lifetime and traffic. In addition, clustering reduces channel contention and facilitates distributed network control, resulting in high network throughput and bandwidth reusability. In the dynamic environments, a major concern in WSNs is adequate sampling of regional data rather than specific node information. Therefore, sensor adaptability is an important factor to WSNs topology control. WSNs adapt the dynamics through applying self-organizing methods to sensor nodes or clusters. A self-stabilizing algorithm [19,20] can lead the system to a legitimate state and keep the network in the
legitimate state unless the network encounters subsequent changes [21– 24]. The authors in [25] proposed a self-stabilizing method to construct the time and energy-efficient routes in large-scale WSNs. The selfstabilizing method can also be applied within intra-clusters to help sensor adapt to the environmental and topological changes, thus enhancing overall energy efficiency. In WSNs, energy efficiency is a critical consideration for design communication protocols not only it is expensive to add new sensors or replace batteries, but also difficult or even impossible to manage all transceivers over the network, such as sensors deployed in battlefields or deep in the ocean. The common objectives of sensor network design include reducing and balancing power consumption for all sensors so as to lengthen network lifetime and maximize sampling quality. Researchers have proposed many strategies and protocols for various scenarios and applications. Amongst these strategies, cluster communication is the most energy efficient while providing controllable transmission delay [25]. The tasks of a CH can be classified into two phases: intra-cluster and inter-cluster communications. In intra-cluster communications, the CHs collect the sensing data from sensors, fuses the data and forward it to other nodes. When the sensor transmits the data to its CH by one hop, it is called a one-hop intra-cluster communication, which requires considerable bandwidth. Furthermore,
∗ Correspondence to: Department of Information Management, National Taichung University of Science and Technology, No.129, Sec.3, Sanmin Rd, North Dist., Taichung City 404, Taiwan, ROC E-mail address: [email protected] (D.-R. Chen).
https://doi.org/10.1016/j.comcom.2019.01.008 Received 12 April 2018; Received in revised form 10 January 2019; Accepted 14 January 2019 Available online 10 February 2019 0140-3664/© 2019 Elsevier B.V. All rights reserved.
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Computer Communications 137 (2019) 15–31
Fig. 1. RF power consumption model.
the CHs incur additional overhead for fusing data from a large number of sensors. The main advantages of multi-hop clustering are a better balanced data processing overhead and a reduction of the required CHs. Therefore, multi-hop intra-cluster communication can be applied to improve network lifetime. Inter-cluster communication amongst CHs and the base station (BS) can also be implemented on either one-hop or multi-hop structures. Multi-hop clustering can also reduce the number of required channels and save energy from long-distance communication [26,27]. However, multi-hop communication usually incurs significant overhead on the CHs surrounding the BS due to the accumulated data payload. The clusters in such an area form a hot-spot, in which they have relatively short lifetimes. Chen et al. first discovered this phenomenon and proposed an unequal cluster-based routing (UCR) [13] to improve the lifetime of CH in hot-spot areas. They determine CHs in energy efficiency on both intra-cluster and inter-cluster communications. Motivated by previous research on clustering algorithms and routing protocols, we study both inter- and intra-cluster routing protocols to balance the lifetime and sensor area of both sensors and clusters. In this paper, we propose a coverage- and energy-aware method with minimum spanning trees (CEMST) that constructs the routes for intra-cluster and inter-cluster transmission respectively using a self-stabilizing algorithm and a minimum spanning tree (MST) algorithm. The proposed intercluster protocol constructs the minimum spanning tree to determine the routes from each cluster to the BS. The intra-cluster protocol considers the deployment of nodes to measure their sensing effectiveness. CMEST determines the radius of clusters based on sensor range, node density, distance and residual energy. It also balances the energy consumption of each node by replacing dying CHs or nodes and adjusting partial MST within a cluster without re-configuration. In other words, CEMST only fixes the routes disconnected due to these dying nodes. The rest of this paper is organized as follows. Section 2 reviews the relevant literature. Section 3 introduces the proposed intra- and intercluster methods. In Section 4, we study the effectiveness of the model parameters and perform simulations to compare the performance of CEMST against other methods. Section 5 presents conclusions.
Fig. 2. Overlapped sensing area of friends of sensor 𝑖.
Hybrid energy-efficient distributed clustering (HEED) approach [29] is similar to LEACH, with CHs chosen in reference to 𝐶𝐻𝑝𝑟𝑜𝑏 = 𝐶𝑝𝑟𝑜𝑏 ×
(2)
where 𝐸𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 and 𝐸𝑚𝑎𝑥 respectively denote the current residual energy and initial energy capacity, and 𝐶𝑝𝑟𝑜𝑏 denotes the expected ratio of CHs to nodes in the network. When the clustering process is triggered, the sensor nodes broadcast their probability to their neighbors and bid for cluster CHs. Other sensors choose their CHs by evaluating the communication cost and number of neighbors. Unequal cluster-based routing (UCR) [13] applies a multi-hop protocol to inter-cluster communications. UCR resolves a hot-spot problem by dividing the network into clusters with different size depending on their distance to the BS. The range of cluster 𝑖 is controlled by 𝑅𝑖 = (1 − 𝑐
𝑑𝑚𝑎𝑥 − 𝑑(𝑠𝑖 , 𝐵𝑆) )𝑅0 𝑑𝑚𝑎𝑥 − 𝑑𝑚𝑖𝑛
(3)
where 𝑅0 and (1 − 𝑐)𝑅0 respectively denote the maximum and minimum cluster range. A constraint c is between 0 and 1, 𝑑(𝑠𝑖 , 𝐵𝑆) denotes the distance between CHs 𝑠𝑖 and BS. Notations 𝑑𝑚𝑎𝑥 and 𝑑𝑚𝑖𝑛 respectively denote the maximum and minimum distance of sensors to the BS. Abdulla et al. propose a hybrid of flat and hierarchical multi-hop routing algorithms to improve the lifetime of sensor networks [13]. Flat multi-hop routing is regarded as a chain-based protocol, and LEACH is a well-known hierarchical multi-hop routing protocol. The hybrid method also considers a network’s hot-spot area, and improves overall network lifetime. The nodes in the hot spot area [13] apply flat multi-hop routing to reduce power consumption due to data fusion. The clusters outside the hot spot area apply hierarchical multi-hop routing to save power from distant transmissions. Flow-balanced routing (FBR) [15,16] is a multi-hop inter-cluster protocol used to achieve power efficiency and coverage preservation. It defines a set 𝐹𝑖 of friends of node 𝑖 in which their sensor areas intersect with that of node 𝑖. FBR takes the sensing area intersection into account and proposes a notion of overlapping degree 1 ⋃ 𝜌𝑖 = 𝐴 ∩ 𝐴𝑗,𝑘 (4) 𝐴𝑖 𝑗,𝑘∈𝐹 𝑖
2. Literature review Low energy adaptive clustering hierarchy (LEACH) [28] is a typical cluster-based protocol for sensor networks. LEACH is one-hop for both inter- and intra-cluster communication. In the setup phase of each round, each node generates a random variable between 0 and 1. If the number is less than a threshold value 𝑇 (𝑛) defined in Eq. (1), the node is chosen as a CH for the current round. ⎧ 𝑘 , 𝐶𝑖 (𝑡) = 1 ⎪ 𝑇 (𝑛) = ⎨ 𝑁 − 𝑘[𝑟 𝑚𝑜𝑑 (𝑁∕𝑘)] ⎪ 𝐶𝑖 (𝑡) = 0. ⎩0,
𝐸𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝐸𝑚𝑎𝑥
(1)
In Eq. (1), k is the expected number of CHs, N is the number of nodes in the network and r is the current round. Function 𝐶𝑖 (𝑡) returns 0 if node 𝑖 has already been a CH and 1 otherwise. All nodes will have been CHs after N/k rounds. The sensor nodes choose their CHs by evaluating the received broadcasting message consisting of the CH’s ID and radio signal strength index (RSSI) from the potential CHs.
𝑖
where 𝐴𝑖 denotes the sensor area of sensor 𝑖 and 𝐴𝑖 ∩ 𝐴𝑗 denotes the overlapping area with adjacent sensing area. Sensors 𝑗 and k denote the 16
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Computer Communications 137 (2019) 15–31
energy, distance to the BS and duration of current CHs. In general, sensors are usually deployed randomly in a plane such that the clusters with loosely deployed member nodes consume much more energy than those with densely deployed nodes. When two clusters have the same distance to the BS, a denser cluster can have a smaller cluster range than that of loose cluster. When two clusters have the same density, the cluster with the longer distance to the BS has the longer cluster range. DBCSA applies a function ⌊ ⌋ 𝑅𝑒 (𝑢) − 𝛾 × 𝐻 (𝑢) , 𝑊 (𝑢) = 𝜙 × 𝐷𝑘 (𝑢) + 𝜑 × 𝐸 (𝑢) 0 ≤ 𝜙, 𝜑, 𝛾 ≤ 1, 𝛾 < 𝜙 + 𝜑 < 1
(5)
to determine whether node u becomes a CH. 𝐷𝑘 (𝑢) is the distance of node u to BS, 𝐷𝑘 (𝑢) is the number of links to other nodes, 𝑅𝑒 (𝑢) is the residual energy of u, 𝐸 (𝑢) is the initial energy and 𝐻 (𝑢) is the duration of the current CH. Three coefficients 𝜙, 𝜑 and 𝛾 are used to adapt to the network diversity. 3. Proposed methods CEMST is composed of three phases: cluster formation, route construction and re-construction. Cluster formation determines the CHs and their members in terms of residual energy and the degree of overlap amongst members. It also determines cluster size in accordance with residual energy, node density and average distance to the BS. In route construction, the intra-cluster routes are constructed using the selfstabilizing algorithm while the inter-cluster routes are constructed using the Borůvka-MST algorithm. The re-construction phase is activated when the CHs die or the intra-cluster links are disconnected due to sensor death. To save energy, it re-connects the disconnected nodes and replaces the dying CHs without cluster re-configuration.
Fig. 3. Example of sensor density.
friends of node 𝑖, i.e., 𝑗, 𝑘 ∈ 𝐹𝑖 . FBR constructs a hierarchical backbone in a graph structure amongst CHs such that they can transmit data in parallel. When the CHs on the backbone die, adjacent nodes can repair the disconnected routes. Balanced clustering algorithm with distributed self-organization (DSBCA) [30] is a multi-hop protocol for both inter- and intra-cluster communication. Its CH selection considers the node density, residual
Fig. 4. An example of cluster formation.
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Fig. 5. Cluster_formation algorithm.
3.1. System model and assumption
3.2. Cluster formation
Before presenting our work, some assumptions and notations are addressed.
Cluster formation is performed in two stages during network initialization. The first stage selects the CHs in accordance with the overlapped sensing area, node density and residual energy. When a CH is located in a high-overlap sensing area, its death does not significantly affect data acquisition quality. The second stage determines the range of each cluster according to its distance to the BS and node density. Let d and r respectively denote the transmission distance and sensing range, and 𝑑 = 𝑟 ⋅ 𝑘 where 𝑘 ≥ 2.
• • • •
A BS is stationary and located at the outer sensing area. Sensors are stationary after deployment. Sensors are identical and labeled with unique identifications (IDs) Each sensor has two modes: active and dormant. In the dormant mode the energy consumption for sensing and communication is ignored. • Transmission power/range can be controlled by sensors, and the distances can be approximated by RSSI measurement given a known transmission power. • Clustering is performed once at network initialization.
Definition 1. Sensors 𝑖 and 𝑗 are friends if there is an overlapping sensing coverage between them. Definition 2. Sensor 𝑗 is a neighbor of sensor 𝑖 if 𝑗 is in the transmission range.
Corresponding to the components in Fig. 1, the RF power consumption model [5,30,31] is defined as { 𝑙𝐸𝑒𝑙𝑒𝑐 + 𝑙𝜀𝑓 𝑠 𝑑 2 , 𝑑 < 𝑑0 𝐸𝑇 𝑥 (𝑙, 𝑑) = 𝐸𝑇 𝑥−𝑒𝑙𝑒𝑐 (𝑙) + 𝐸𝑇 𝑥−𝑎𝑚𝑝 (𝑙, 𝑑) = 𝑙𝐸𝑒𝑙𝑒𝑐 + 𝑙𝜀𝑚𝑝 𝑑 4 , 𝑑 ≥ 𝑑0
Notably, a sensor with many neighbors does not necessarily have many friends. An example of an overlapping sensor area of sensor 𝑖 is shown in Fig. 2. The ratio of overlapped sensing area 𝜌𝑖 is defined as
(6)
𝜌𝑖 =
where 𝑙 is the length of the transmission √data in bits, and d denotes the distance to the receiver. Notation 𝑑0 =
𝜀𝑓 𝑠
𝜀𝑚𝑝
𝐴𝑖
, 1 > 𝜌𝑖 > 0,
(8)
where 𝐴𝐹𝑖 denotes the sensing area of the friends of sensor 𝑖, and 𝐴𝑖 denotes the sensing area of sensor 𝑖. Assuming 𝑘 = 2 and all sensors are randomly deployed in a plane according to uniform distribution, an adequate density for sensor 𝑖 is
denotes reference distance
where 𝜀𝑓 𝑠 and 𝜀𝑚𝑝 are respectively the amplifier power required in free space and multi path propagation. The power loss of both the free space (𝑑 2 ) and multi-path fading (𝑑 4 ) channel model are based on the distance between the transceivers. When a node receives 𝑙 bits of data, it requires energy 𝐸𝑅𝑥 (𝑙) = 𝐸𝑅𝑥−𝑒𝑙𝑒𝑐 (𝑙) = 𝑙𝐸𝑒𝑙𝑒𝑐 .
𝐴𝑖 ∩ 𝐴𝐹𝑖
𝐶 (𝑖) =
|𝐹 (𝑖)| ≅ 0.25, |𝑁(𝑖)|
(9)
where |𝐹 (𝑖)| and |𝑁(𝑖)| respectively denote the number of friends and neighbors of sensor 𝑖. Given 𝑑 = 𝑟 × 𝑘 and 𝑘 = 2, the transmission
(7) 18
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Computer Communications 137 (2019) 15–31
density 𝐶 (𝑖) denotes the ratio of friends to the neighbors in a sensor’s transmission range. The notion of density 𝐶 (𝑖) is used to balance the power consumption amongst a node and its neighbors, while 𝜌𝑖 is used to ease the quality degradation of data acquisition from its friends. Since CHs require additional power for collecting and processing the sampling data from its members, the percentage of residual energy is ⌊ ⌋ 𝐸𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 (𝑖) 𝐸 (𝑖) = × 100%, (10) 𝐸𝑚𝑎𝑥 (𝑖) where 𝐸𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 (𝑖) and 𝐸𝑚𝑎𝑥 (𝑖) respectively denote the residual and initial energy of node 𝑖. In accordance with Eqs. (8)–(10), a function is derived as Fig. 6. Intra_self-stabilizing algorithm.
𝑊 (𝑖) = 𝜌𝑖 × 𝛼 + (𝐶 (𝑖) − 0.25) × 𝛽 + 𝐸 (𝑖) × 𝛾,
0.1 ≤ 𝛼, 𝛽, 𝛾 ≤ 1
(11)
where coefficients 𝛼, 𝛽 and 𝛾 are used to control the proportion of each item. The notations In Eq. (11) are regarded as three parts which can be tuned in terms of different requirements using 𝛼, 𝛽 and 𝛾. In the first part 𝜌𝑖 ×𝛼, 𝜌𝑖 presents the ratio of overlapped sensing area to the sensing area of node 𝑖. Coefficient 𝛼 controls the importance of 𝜌𝑖 in 𝑊 (𝑖). When 𝜌𝑖 is high, the nodes may be densely deployed in their sensing area and more CHs can be elected without influence of sampling quality. Additionally, if 𝜌𝑖 is higher than a threshold 𝑇𝑑𝑒𝑛𝑠𝑒 , node 𝑗 is switch to dormant mode for saving mover energy and prolonging network lifetime. Those CHs are applied to process and forward data, shorten the transmission range of sensors and balance the load/lifetime of the sensors. In the second part, (𝐶 (𝑖) − 0.25) × 𝛽 considers the density of friend and neighbor nodes in the transmission areas of node i. This part is used to balance the power consumption among its neighbors, and is tuned by 𝛽. The notation 𝐸 (𝑖) in the third part introduced widely by many clustering algorithms is controlled by 𝛾 in the CEMST.
area of a node is approximately four times the size of its sensing area shown in Fig. 3. When the sensor nodes are deployed on the area in uniform distribution, the transmission range covers about four times of nodes as those in the sensing area. In other words, |𝑁(𝑖)| must be around four times larger than |𝐹 (𝑖)| so as to correspond to the assumed node distribution. If the adequate density ratio does not follow the distribution, say 𝐶 (𝑖) = 0.5, system would overestimate the required number of sensors. In the area, none of the sensors can switch to dormant mode when actual density is about 0.5, resulting in over sampling and energy inefficiency. In case sensor deployment applies other distribution, the model of 𝐶 (𝑖) may need to redesign. However, a deployment with non-uniformed distribution may not suitable for practical use. Fig. 3(a) shows the case of 𝐶 (𝑖) ≅ 0.25, where the transmission area is about four times the sensing area, as is the number of nodes. The cases 𝐶 (𝑖) ≪ 0.25 and 𝐶 (𝑖) ≫ 0.25 respectively shown in Fig. 3(b) and (c), hamper a cluster’s energy balance. In Fig. 8(b), node 𝑖 will exhaust its battery power faster than its neighbors because it must process and forward data from them. Similarly in Fig. 8(c), node 𝑖 would consume more energy than its friends. Different from 𝜌𝑖 ,
The cluster range is determined by ⌊ ⌋ 𝐷 (𝑖) 𝑅𝑖 = 𝜑 × , 𝐶 (𝑖)
Fig. 7. An example of the intra_self-stabilizing algorithm.
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(12)
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Fig. 8. Example of MST construction among CHs.
Fig. 9. Inter-cluster_MST algorithm.
Fig. 11. Example of CH replacement.
Fig. 10. Example of inter-cluster labeling.
Fig. 12. CH_replacement algorithm.
where 𝜑 ≥ 2𝑟 prevents 𝑅𝑖 from being less than sensor range 𝑟. Notation
𝐷 (𝑖) = (1 −
𝑑𝑖 ) 𝑑𝑚𝑎𝑥
(13)
members, that is ⌊ ⌋ 𝐷 (𝑖) 𝑅𝑖 = 𝜑 × ≥ 𝑇 ℎ. 𝐶 (𝑖)
denotes the distance function of node 𝑖 to BS and affects the size of a cluster where 𝑑𝑖 is the distance of CH i to BS. Notation 𝑑𝑚𝑎𝑥 is the maximum distance between a node and BS in the network. When a CH 𝑖 is far away from BS, the value of D(𝑖) could be very small and result in a larger cluster. In other words, the CEMST reduces the cluster range as the node density increases or the cluster is adjacent to BS. Let 𝑇 ℎ be an upperbound of 𝑅𝑖 such that the CHs are not isolated or do not lack for
The upper-bound of the distance to BS is derived as ) ⌋ ⌊( 𝐶 (𝑖) 𝑑𝑖 ≤ 1 − 𝑇ℎ × 𝑑𝑚𝑎𝑥 . 𝜑
(14)
(15)
If a node with high value of 𝑊 (𝑖) does not satisfy Eq. (15), it cannot be chosen as a CH because it may be located far away. Algorithm 20
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Fig. 14. Route_re-construction Algorithm.
cluster_formation is shown in Fig. 5 and an example of the algorithm is depicted in Fig. 4. In Fig. 4(a), all sensor nodes performed by initialization phase receive a signal CLUSTER_FORM (t ) with in a time constraint t from BS and try to find their friends and neighbors. In accordance with the surrounding information, they compute the value derived from the function W (𝑖) in Eq. (11). A countdown timer starting to the time constraint limits the initial clustering duration. In the phase of State_Determination, each node bids for CH (lines 2–4) when they have the highest weighting value among their neighbors and satisfy the distance constraint to BS in Eq. (15). An example for node 𝑖 is shown in Fig. 4(b), each node also determines their friends and neighbors. In line 5, each CH updates its transmission range and broadcasts a HEAD signal to its neighbors and exit the phase. An example is shown in Fig. 4(c). In lines 6–7, other non-CH nodes receiving a HEAD signal become the members of the signal source. When a node 𝑗 with 𝜌𝑗 higher than density threshold 𝑇𝑑𝑒𝑛𝑠𝑒 , it will be switched to the sleep mode. In
Fig. 13. Example of node reconnection.
Fig. 15. Clustering structure with 200 nodes.
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Fig. 16. Clustering structure with 300 nodes.
lines 8–9, the isolated nodes join the CH with the shortest distance to them. Finally, CH 𝑖 reports the clustering results to BS, and a cluster is formed. The example is shown in Fig. 4(d). Notably, Algorithm 1 does not construct transmission paths. The path construction is performed by self-stabilizing algorithm.
We define a connected graph G = (V, E) which consists of a set V of CHs, and a set E of links labeled with a distance between two CHs. To derive the shortest paths among clusters, we construct a MST based on Borůvka’s Algorithm as follows. 1. Initially, each node denotes a tree T. 2. Each tree T finds the shortest edge to other trees, which does not cause any loop. 3. Combine the trees to form a bigger tree. 4. Repeat steps 2 and 3 until an MST of G is produced.
3.3. Route construction This section presents intra- and inter-cluster communication methods. A self-stabilizing algorithm is used to construct the shortest paths within a cluster, which does not need network initialization and provides fault-tolerance that satisfies the independent operation of individual clusters. Let G = (V, E) be a connected graph and represent a cluster. E and V respectively denote a set of all sensors and edges, where {∀{𝑖, 𝑗} ∈ 𝐸|𝑖, 𝑗 ∈ 𝑉 and 𝑖 ≠ 𝑗}. Notation d(𝑖) of sensor 𝑖 records the length of the shortest path, r denotes a CH, M denotes a set of sensor nodes in the cluster, and S denotes a set of nodes which already derive their shortest paths. The proposed self-stabilizing algorithm shown in Fig. 6 is similar to Dijkstra’s algorithm [19], which constructs the singlesource shortest-path in a cluster. The example of self-stabilizing algorithm is presented in Fig. 7. Node 5 is true for R0, nodes 2 and 3 are also true for R1. In Fig. 7(b), node 6 is true for R1 such that 𝑑(6) = 15. In Fig. 7(c), node 7 is true for R1 and has the two shortest paths. It selects the link to the neighbor with the maximum residual energy, and therefore node 7 derives 𝑑(7) = 20. Finally, in Fig. 7(d), the nodes in a cluster reach a steady state and derive their shortest paths.
In Fig. 8(a), for example, CHs broadcast a message to measure the distance to its neighbors. By repeating stages 2 and 3, the subtrees are emerged in Fig. 8(b). In Fig. 8(c), the subtrees can be combined using edge {e, b} with the minimum distance between the two trees. Finally, an MST can be constructed amongst CHs and BS. Fig. 9 presents the inter-cluster_MST algorithm. In line 1 of the algorithm, each CH is initially regarded as a component in a set T. A while-loop in lines 2–7 is applied to process the components in T until only one component is left. In lines 3–6, each component is drawn from T and records its edge with the minimum weight using a set S, where the edges must be connected to the CHs in other components. After adding an edge to S, its two connected components are combined as one component. Finally, an inter-cluster MST is constructed using the edges in S. To forward the sensor data to the BS, a MST is rooted in the BS and each CH is labeled in a hierarchical order shown in Fig. 10. The BS labeled with P(0) is at level 0. In level 2, CHs are labeled in increasing order from left to right and so on for the next level. 22
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Fig. 17. Clustering structure with 400 nodes.
Fig. 18. Effectiveness of 𝜌𝑖 .
Fig. 19. Effectiveness of 𝐶 (𝑖).
3.4. Intra-cluster re-routing algorithms to the cluster members and perform algorithm Intra_self-stabilizing in Fig. 6 to construct the new routes. After algorithm CH_replacement is accomplished, the old CH is recovered to a member sensor. The result is shown in Fig. 11(d), and algorithm CH_replacement is shown in Fig. 12. When the residual energy 𝑅𝑒 (𝑗) of CH 𝑖 is lower than a threshold, algorithm CH_replacement is performed to elect a new CH. In line 1, a CH broadcasts a HELP signal to its cluster members, and they will re-compute their values of W (𝑗) according Eq. (11). In line 3, a new CH is elected in terms of W (𝑗) and broadcasts a HEAD signal to the
As mentioned earlier, the proposed method does not require cluster re-configuration as CHs die. The CH_replacement algorithm is applied to replace dying CHs, and route_re-construction algorithm is used to repair routes disconnected due to dead nodes. In Fig. 11(a), CH_replacement is initiated when the residual energy 𝑅𝑒 (𝑗) of a CH less than a threshold 𝑟𝑡ℎ . In Fig. 11(b), the dying CH broadcasts a HELP message to all cluster members, including dormant nodes. In Fig. 11(c), each node 𝑖 computes its W (𝑖), and, excluding dying CHs, the node with the maximum value of W (𝑖) is selected as a new CH. The new CH broadcasts a HEAD message 23
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Fig. 20. Lifetime of CEMST, DSBCA and FBR with 𝛼 = 0.5 𝛽 = 0.4 and # nodes = 200.
Fig. 22. Lifetime of CEMST, DSBCA and FBR with 𝛼 = 0.5 𝛽 = 0.4 and #nodes = 400.
Fig. 21. Lifetime of CEMST, DSBCA and FBR with 𝛼 = 0.5 𝛽 = 0.4 and #nodes = 300.
Fig. 23. Coverages of CEMST, DSBCA and FBR with 𝛼 = 0.5, 𝛽 = 0.4 and #nodes = 200.
other cluster members. The former CH 𝑖 then invokes algorithm route_reconstruction to adjust the path to its adjacent nodes. When a node is almost running out of energy, route_re-construction algorithm is invoked by the node to broadcast a DEATH message to its friends. The friends calculate their respective W (𝑖) values and a friend with the maximum W (𝑖) value becomes a connector. The other friends will reconnect to this connector and proceed with their sensor tasks. Notably, a connector is a sensor member and cannot be the current CH. In the example shown in Fig. 13(a) and (b), a node 𝑗 is running out energy and broadcasts a DEATH message to its friends k, l and 𝑖. In Fig. 13(c), k, l and 𝑖 calculate their W (𝑖) values according to Eq. (11), and node 𝑖 is chosen as a connector. Finally, in Fig. 13(d), friends k and l change their connections to node 𝑖. Fig. 14 shows the route_re-construction algorithm. In line 1, a dying CH 𝑗 broadcasts a DEATH signal to its adjacent nodes, i.e., the directly connected nodes. In line 2–4, all the adjacent nodes re-compute their values of W (𝑗) according Eq. (11) and connect to their adjacent nodes with the highest value of W (k), 𝑘 ≠ 𝑗. Finally, in line 5, nodes k are notified to connect node 𝑗. In a multi-hop cluster, the nodes in the cluster border rely on their neighbors to forward their data to CH. When a CH is dead, its member nodes do not disappear or dismiss on their existing MST path due to selfstabilizing feature. The disconnected MST path can still work because a new CH can be elected by CH_replacement algorithm and the MST can be re-connected by route_re-construction algorithm. CEMST has already constructed the energy efficient clusters and MST paths during initial phase, assuming the network is composed of identical nodes uniformly deployed in the target area. Therefore, the nodes in the border switching to another cluster do not improve average energy efficiency of the network even if the number of dead nodes increases resulting a sparse network.
Fig. 24. Coverages of CEMST, DSBCA and FBR with 𝛼 = 0.5, 𝛽 = 0.4 and #nodes = 300.
Fig. 25. Coverages of CEMST, DSBCA and FBR with 𝛼 = 0.5, 𝛽 = 0.4 and #nodes = 400.
24
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Fig. 26. Relationship between lifetime and cluster range with #nodes = 200.
4. Performance evaluation
4.1. Experimental assumptions and settings The simulations are developed using MATLAB. The parameter values used in the simulations are shown in Table 1, and the performance metrics are examined with various coefficients mentioned in Section 3. A network scenario with a 100 × 100 m2 sensor area is used to simulate 200, 300 and 400 sensor nodes randomly deployed in the sensor area. The sink node, i.e. the BS, is located at 120 m, 50 m with an infinite power supply, and all sensors have an initial energy of 0.5 J. Experiment durations are performed in units of round similar to [16,30]. At the beginning of each round, FBR and CEMST do not require cluster reconfiguration. FBR must perform routing algorithm for each packet, and CEMST only performs CH_replacement and route_re-construction for a cluster when a node dies.
In this section, we evaluate the performances of CEMST, FBR and DSBCA and derive the effectiveness of the parameters applied by the proposed method. Experimental results provide suitable parameter settings for the proposed method. The experiments measure: (1) Parameter effectiveness: We simulate the target methods in terms of cluster ranges, i.e.,
𝑅(𝑖) , 2𝑟
node density C(𝑖) and degree of
overlap 𝜌𝑖 . (2) Performance: We compare clustering structures produced by CEMST, DSBCA and BFR in different network sizes. The effectiveness of the cluster range and network lifetime are discussed
4.2. Clustering structure
when the first, 10%, 20%, 30% and 40% sensors die. Also, the coverage ratio of the target methods are also compared in terms
Figs. 15–17 respectively show simulations of cluster structures with 200, 300 and 400 nodes. The clustering structure of CEMST is relatively
of network lifetime. 25
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Fig. 26. (continued). Table 1 Parameters used in simulations. Parameters
Values
Sensor area BS location Number of sensor nodes Initial energy of sensor nodes Packet size
100 m × 100 m (120, 50) 200, 300, 400 0.5 J 2000 bits 𝑇 𝑟𝑎𝑛𝑠𝑚𝑖𝑡 ∶ { 𝑙𝐸𝑒𝑙𝑒𝑐 + 𝑙𝜀𝑓 𝑠 𝑑 2 , 𝑑 < 𝑑0 𝐸𝑇 𝑥 (𝑙, 𝑑) = 𝑙𝐸𝑒𝑙𝑒𝑐 + 𝑙𝜀𝑚𝑝 𝑑 4 , 𝑑 ≥ 𝑑0
Energy consumption model
a CH consumes more energy for fusion and forwarding the data from its neighbor nodes. The maximum distance between CHs and BS (max_dist ) dominates the network latencies because CHs transmit a large amount of data through a longer distance than an intra-cluster communication. In Fig. 15, the networks with 200 nodes are simulated using FBR, DSBCA and CEMST. CEMST produces the smallest number of cluster (#cluster = 19) and has the smallest maximum CH degree of all other methods (max_deg = 4). The example with spars nodes shows that CEMST can achieve better energy efficiency than other methods. In the example, CEMST requires up to 9 hops (max_dist = 9) from a CH to BS and outperforms other methods in transmission latency. Comparing Fig. 16(c) with Fig. 16(a) and (b), CEMST also requires the fewest clusters (#cluster = 22) and produces the smallest max_deg (= 4) of CHs amongst all other methods. In addition, CEMST has the shortest maximum inter-cluster routes (max_dist = 9) than FBR and DSBCA thus improving energy efficiency and network lifetime. In the high density node deployments shown in Figs. 16 and 17, CEMST more obviously outperforms other methods in energy efficiency because the values of #cluster (= 22 and 24) are slowly increased along with node density. Also, the values of max_deg (= 4) of CHs is unchanged which benefit the management of cluster members. Compare to other methods, their #cluster and max_deg increase rapidly with the node density, which their energy efficiency. In the examples, CEMST also produces the most stable max_dist (= 9 and 10) and therefore the lowest intercluster transmission latency. The cluster decision of DSBCA is based on residual energy and link density, which is highly dependent on sensor deployment. Therefore, the number of clusters may increase due to uneven sensor deployment. In CEMST, the CHs are determined by a weighting function considering sensor overlapping ratio, node density and residual energy. The range of clusters also considers their distance to the BS. The inter-cluster routes amongst the CHs are constructed using the MST algorithm, and thus the number of CHs is controlled by sensor coverage and energy efficiency. To prevent ‘‘Hot Spot’’ effect, Eq. (11) applies C(𝑖) to determine the number of CHs in an area with
𝑅𝑒𝑐𝑒𝑖𝑣𝑒 ∶ Transmit, receiving energy consumption per bit 𝐸𝑒𝑙𝑒𝑐 Amplifier power consumption 𝜀𝑓 𝑠 (𝑑 < 𝑑0 ) Amplifier energy consumption 𝜀𝑚𝑝 (𝑑 ≥ 𝑑0 ) Data compression ratio Energy threshold ratio (𝑟𝑡ℎ ) Data fusion energy (EDA)
𝐸𝑅𝑥 (𝑙) = 𝑙𝐸𝑒𝑙𝑒𝑐 50 nJ/bit 10 pJ/bit//m2 0.0013 pJ/bit/m4 0.7 0.3 5 nJ/bit
better balanced. We can see that FBR applies single-hop transmission to the intra-cluster, resulting in significant CH overhead and requiring additional backup CHs, thus reducing sensing accuracy and energyefficiency. DSBCA and CEMST apply multi-hop to intra-cluster communications, and construct tree-based structures for inter-cluster routing. We discuss three metrics #clusters, max_deg and max_dist to evaluate the performance of energy efficiency, balance and latency of underlying cluster structures. A network with a number of clusters (#cluster) requires #cluster CHs that spend additional energy to manage their cluster members and forward data, which disable their sensing tasks and hamper their service and energy balance. The maximum degree of a CH (max_deg ) defines the largest number of sensors that directly connect to the CH in a network. Along with a higher value of max_deg, 26
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Fig. 27. Relationship between lifetime and cluster range with #nodes = 300.
uneven nodes so as to balance the power consumption of CHs. R(𝑖) helps
4.3. Effectiveness of the cluster parameters
determine the range of the clusters in terms of their distances to BS. For In accordance with function 𝑊 (𝑖) in Eq. (11), coefficients 𝛼 and 𝛽 are used to control the impact of the parameters 𝜌𝑖 and 𝐶 (𝑖) and examine their effectiveness in the networks with 100 sensor nodes. Each combination of parameters is performed 100 times. It is known that 𝐸 (𝑖) directly affects sensor lifetime and thus its initial value is 1 in the simulations. Figs. 18 and 19 present the effectiveness of 𝜌𝑖 and 𝐶 (𝑖), respectively. On average, Fig. 18 presents that a low value of 𝛼 would decrease communication rounds by 5% when 40% nodes die comparing to high value of 𝛼. When the first node dies, a low value of 𝛼 would decrease communication rounds by an average of 14%. We can conclude that, when the sampling quality is not prioritized, low sensor coverage would benefit network lifetime. In other words, we can trade the network lifetime with sampling quality in the high percentage of dead sensors. On the contrary, Fig. 19 shows that 𝛽 controlling 𝐶 (𝑖) has insignificant effectiveness on the number of dead nodes. The main reason is that, Algorithm 1 would switch the nodes locating in high density of sensing coverage to dormant mode. Therefore, the sensor
example, in Fig. 15(c), the node in position x = 90, y = 35 is belong to the CH in position x = 80, y = 19. However, there are CHs closer, like the one in x = 80, y = 40. The reason is that the CHs in position x = 80, y = 39 and x = 86, y = 52 are located among densely nodes. In addition, they are close to BS than the CH in position x = 80, y = 19. In accordance with the proposed C(𝑖) and R(𝑖), their ranges and the number of members should be smaller than the CH in position x = 80, y = 19. Therefore, the node in position x = 90, y = 35 is excluded by those two CHs. Other situation such as the node in position x-42, y = 62 has a CH next to it. CEMST regards data transmission range without data receiving precedence due to self-stabilizing algorithm. The sensor node in x = 42, y = 62 may receive HEAD signals from the CH in x = 55, y = 75 earlier than that from the CH of x = 40, y = 62, and it becomes the member of the CH in x = 55, y = 75. 27
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Fig. 27. (continued).
coverage in CEMST should be uniformed and does not required to be changed during the network operation time to simplify the sensor and software design.
4.5. Effectiveness of cluster coverage This section discusses the cluster coverage ratio of FBR, DSBCA and CEMST in the range of 100–700 rounds. The simulations are performed with 200, 300 and 400 nodes deployed in the 100 × 100 m2 sensing area in Figs. 23–25, respectively. The results show that CEMST produces higher cluster coverage ratio than those of FBR and DSBCA. FBR is a coverage-aware protocol and slightly outperforms the multi-hop protocol DSBCA in a large-scale network (#nodes = 400). In a largescale network, randomly deployed nodes may overlapped their sensing areas in a high proportion. A node with high proportion overlapped area is assigned as a CH that balances high transmission load of other CHs. The CH does not degrade cluster coverage and sampling quality while balancing the nodes lifetime. Additionally, some of the nodes with high values of 𝐶 (𝑖) and 𝜌𝑖 can be put into dormant mode to reduce energy consumption and cannot affect the coverage ratio. To compare Figs. 23–25 respectively with Figs. 26–28 corresponding to the same communication rounds, we also have the following observations. In Fig. 23, CEMST achieves about 50% coverage in 40% dead nodes among 200 nodes. In contrast, FBR and DSBCA have less 20% coverage when there are 40% of dead nodes. In Fig. 24, CEMST achieves about 70% coverage in 40% dead nodes among 200 nodes, and the coverages achieved by FBR and DSBCA are less than 60%. Corresponding Fig. 25 with Fig. 22, CEMST reaches 87% coverage when 40% nodes die and outperforms the coverages of FBR and DSBCA.
4.4. Network lifetime In this section, the network lifetime using FBR, DSBCA and CEMST are evaluated when the first, 10%, 20%, 30% and 40% of nodes die. We simulate network operations in 200, 300 and 400 nodes deployed in a 100 × 100 m2 sensor area. Each combination of parameters 𝛼 and 𝛽 is performed in experiments 50 times to calculate the average number of communication rounds. In Fig. 20, we simulate 200 sensor nodes with coefficients 𝛼 = 0.1 and 𝛽 = 0.4, finding that CEMST outperforms FBR and DSBCA for all proportions of dead nodes. DSBCA outperforms FBR when the proportion of dead nodes is less than or equal to 20%, because DSBCA considers the link density. In other words, when a CH locates in low-density region, the multi-hop cluster range should be expanded. As the number of dead sensors increases (e.g., 30%), the existing nodes have to consume more power to transmit data to their CHs. Meanwhile, in FBR, the nodes in the one-hop cluster do not spend more power to transmit data to their CHs, and therefore FBR outperforms DSBCA when the percentage of dead nodes exceeds 20%. CEMST is a multi-hop clustering approach and can change partial routes in a cluster when a node dies without cluster re-configuration, and therefore it outperforms FBR and DSBCA. Figs. 21 and 22 show the average rounds of communication in 300 and 400 nodes, respectively. For the same rounds, CEMST has the lowest ratio of dead nodes compared to the other methods, and the same conclusion follows. Compared with Fig. 20, DSBCA outperforms FBR in terms of communication rounds when the percentage of dead nodes is less than or equal to 30% and 40%, respectively, in Figs. 21 and 22. We can conclude that one-hop cluster communication hampers network lifetime given a large number of nodes in the network. In Figs. 20–22, the differences amongst the underlying methods in terms of the average number of rounds decreases as the network size increases.
4.6. Effectiveness of cluster range Cluster range is an important factor in determining WSN network lifetime. We study how maximum cluster range affects the performance of the target methods in a 100 × 100 m2 area. Figs. 26–28 show the average number of communication rounds for the various methods for 50 experiments with different maximum cluster ranges. The range of control parameters are 𝛼 = [0.1, 1.0], 𝛽 = [0.1, 1.0] and 𝛾 = 1. According 28
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Fig. 28. Relationship between lifetime and cluster range with #nodes = 400.
to Eq. (14), the cluster range RiMax in terms of sensor radius r changes from 2r to 7r and 𝑇 ℎ = 7𝑟. The cluster ranges are not controlled by FBR, while those in DSBCA and CEMST are controlled by the distance to the BS and respectively by link density and node density. Although the node density defined in Eq. (9) is different from the link density, they are both designed to resolve hot-spot problem and prolong network lifetime. Fig. 26(a)–(e) respectively present the experimental results for 200 nodes with the death of the first node along with the deaths of 10%, 20%, 30% and 40% of all nodes. When the first node dies, FBR does not change with cluster range and has the lowest lifetime. The lifetime of CEMST is longer than those of the other methods and increases with cluster range while the trend of DSBCA is not obvious. In Fig. 26(b)–(e), the network lifetimes of all methods increase with cluster ranges, and FBR increases more rapidly with the percentage of dead nodes. Fig. 27(a)–(e) simulate 300 nodes in a 100 × 100 m2 area and show the average lifetime. Both DSBCA and FBR are not sensitive to the cluster ranges when the first node dies, and CEMST still outperforms other methods in all cluster ranges. Except for the first node death presented in Fig. 27(a), FBR still increases the most rapidly of all methods. It
outperforms DSBCA when 40% nodes die and the cluster range exceeds 5r. Compared with Fig. 26, the growth of the lifetime in Fig. 27 is relatively slow as cluster range increases. Fig. 28(a)–(e) simulate a network with 400 nodes. FBR is worse than other methods for all cluster ranges. Figs. 26–28 show that CEMST achieves the best performance under various conditions, and follow a similar trend as that shown in Fig. 27. In the worst case, Fig. 28(e) shows that CEMST incurs 403 and 592 communication rounds when 40% of nodes die, but this is still better than the other two methods. In Figs. 26–28, as increasing the number of dead nodes results in small performance differences among the underlying methods. 5. Conclusions This paper proposes a coverage- and energy-aware method with minimum spanning trees (CEMST) for multi-hop cluster WSNs. In CEMST, cluster size is elaborated with the complete experiments, and the effectiveness of the proposed parameters such as degree of sensor overlap, node density are examined. The distance to the BS is employed 29
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Fig. 28. (continued).
to balance node energy consumption using self-organizing algorithms. Compared with the existing methods, CEMST constructs more suitable cluster structures and energy-efficient routes. When a node or CH dies, CEMST adjusts the local routes of the cluster and replaces the CH without re-configuration, thus extending network lifetime. Simulation results show that CEMST outperforms other methods in cluster coverages and network life time, and is scalable for networks with different cluster sizes to achieve energy efficiency.
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Acknowledgments The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. MOST 105-2221-E-025-007 – and MOST MOST 106-2221-E-025-003 – and Taichung Veterans General Hospital of the Republic of China, Taiwan Department of Medical Research project for financially support under Contract No. TCVGHNTUST1088503. References [1] D. Li, K.D. Wong, Y.H. Hu, A.M. Sayeed, Detection, classification, and tracking of targets, IEEE Signal Process. Mag. 19 (2) (2002) 17–29. [2] Y. Durmus, A. Ozgovde, C. Ersoy, Distributed and online fair resource management in video surveillance sensor networks, IEEE Trans. Mob. Comput. 11 (5) (2012) 835–848. [3] H. Modares, R. Salleh, A. Moravejosharieh, Overview of security issues in wireless sensor networks, in: Computational Intelligence, Modelling and Simulation (CIMSiM), 2011 Third International Conference on, IEEE, 2011, pp. 308–311. [4] P.N. Reddy, P.I. Basarkod, S.S. Manvi, Wireless sensor network based fire monitoring and extinguishing system in real time environment, Int. J. Adv. Netw. Appl. 3 (2) (2011) 1070. [5] D.R. Chen, W.M. Chiu, Collaborative link-aware protocols for energy-efficient and qos wireless body area networks using integrated sensors, IEEE Internet Things J. 5 (1) (2018) 132–149. [6] S. Tilak, N.B. Abu-Ghazaleh, W. Heinzelman, A taxonomy of wireless micro-sensor network models, Mobile Comput. Commun. Rev. 6 (2) (2002) 28–36. 30
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Contents lists available at ScienceDirect
Computer Communications journal homepage: www.elsevier.com/locate/comcom
A coverage-aware and energy-efficient protocol for the distributed wireless sensor networks Da-Ren Chen a ,∗, Lin-Chih Chen b , Mu-Yen Chen a , Ming-Yang Hsu a a b
Department of Information Management, National Taichung University of Science and Technology, Taichung, Taiwan, ROC Department of Information Management, National Dong Hwa University, Hualien, Taiwan, ROC
ARTICLE
INFO
Keywords: Wireless sensor network Residual energy Dijkstra shortest path algorithm Minimum spanning tree Borůvka algorithm Distributed algorithm Self-stabilizing algorithm
ABSTRACT Wireless sensor network (WSNs) are composed of a large number of battery-powered wireless sensors, which acquire and monitor physical data from their surroundings through self-organization. The sensors are deployed randomly in a target area where maintenance and battery replacement are difficult or even impossible. To achieve better coverage and prolong network lifetime, networks typically adopt clustering protocols with hierarchical inter-cluster topology for network management and data acquisition in WSNs. However, typical solutions require cluster re-configuration due to early death of cluster heads (CHs) and cause energy inefficiency. This paper proposes a coverage- and energy-aware protocol with intra- and inter-cluster methods called CEMST that considers the sensor node density and coverage overlapping. In addition, to adapt network dynamics while improving energy efficiency, self-stabilizing algorithm and Borůvka algorithm are applied to construct the minimum spanning trees (MST) for intra- and inter-cluster routes, respectively. Simulation results indicate that CEMST produces the balanced clustering structures and provides better coverage and longer network lifetime than previous methods.
1. Introduction The development of tiny, inexpensive and versatile sensors with wireless transceivers has been driven by the increasing use of wireless sensor network (WSN) applications. A WSN consists of a number of sensor nodes capable of data acquisition, processing and transmission. WSNs have been extensively implemented in industrial, civilian and military applications [1–7]. Due to bandwidth and energy constraints, the sensor nodes cannot communicate directly with other sensors over long distances. A multihop protocol is needed to transmit data from a sensor to a base-station out of the sensor’s transmission range. Cluster-based methods [8–18] provide better energy efficiency than multi-hop protocol by using data fusion within a cluster head (CH), thus decreasing data transmission and improving network lifetime and traffic. In addition, clustering reduces channel contention and facilitates distributed network control, resulting in high network throughput and bandwidth reusability. In the dynamic environments, a major concern in WSNs is adequate sampling of regional data rather than specific node information. Therefore, sensor adaptability is an important factor to WSNs topology control. WSNs adapt the dynamics through applying self-organizing methods to sensor nodes or clusters. A self-stabilizing algorithm [19,20] can lead the system to a legitimate state and keep the network in the
legitimate state unless the network encounters subsequent changes [21– 24]. The authors in [25] proposed a self-stabilizing method to construct the time and energy-efficient routes in large-scale WSNs. The selfstabilizing method can also be applied within intra-clusters to help sensor adapt to the environmental and topological changes, thus enhancing overall energy efficiency. In WSNs, energy efficiency is a critical consideration for design communication protocols not only it is expensive to add new sensors or replace batteries, but also difficult or even impossible to manage all transceivers over the network, such as sensors deployed in battlefields or deep in the ocean. The common objectives of sensor network design include reducing and balancing power consumption for all sensors so as to lengthen network lifetime and maximize sampling quality. Researchers have proposed many strategies and protocols for various scenarios and applications. Amongst these strategies, cluster communication is the most energy efficient while providing controllable transmission delay [25]. The tasks of a CH can be classified into two phases: intra-cluster and inter-cluster communications. In intra-cluster communications, the CHs collect the sensing data from sensors, fuses the data and forward it to other nodes. When the sensor transmits the data to its CH by one hop, it is called a one-hop intra-cluster communication, which requires considerable bandwidth. Furthermore,
∗ Correspondence to: Department of Information Management, National Taichung University of Science and Technology, No.129, Sec.3, Sanmin Rd, North Dist., Taichung City 404, Taiwan, ROC E-mail address: [email protected] (D.-R. Chen).
https://doi.org/10.1016/j.comcom.2019.01.008 Received 12 April 2018; Received in revised form 10 January 2019; Accepted 14 January 2019 Available online 10 February 2019 0140-3664/© 2019 Elsevier B.V. All rights reserved.
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Fig. 1. RF power consumption model.
the CHs incur additional overhead for fusing data from a large number of sensors. The main advantages of multi-hop clustering are a better balanced data processing overhead and a reduction of the required CHs. Therefore, multi-hop intra-cluster communication can be applied to improve network lifetime. Inter-cluster communication amongst CHs and the base station (BS) can also be implemented on either one-hop or multi-hop structures. Multi-hop clustering can also reduce the number of required channels and save energy from long-distance communication [26,27]. However, multi-hop communication usually incurs significant overhead on the CHs surrounding the BS due to the accumulated data payload. The clusters in such an area form a hot-spot, in which they have relatively short lifetimes. Chen et al. first discovered this phenomenon and proposed an unequal cluster-based routing (UCR) [13] to improve the lifetime of CH in hot-spot areas. They determine CHs in energy efficiency on both intra-cluster and inter-cluster communications. Motivated by previous research on clustering algorithms and routing protocols, we study both inter- and intra-cluster routing protocols to balance the lifetime and sensor area of both sensors and clusters. In this paper, we propose a coverage- and energy-aware method with minimum spanning trees (CEMST) that constructs the routes for intra-cluster and inter-cluster transmission respectively using a self-stabilizing algorithm and a minimum spanning tree (MST) algorithm. The proposed intercluster protocol constructs the minimum spanning tree to determine the routes from each cluster to the BS. The intra-cluster protocol considers the deployment of nodes to measure their sensing effectiveness. CMEST determines the radius of clusters based on sensor range, node density, distance and residual energy. It also balances the energy consumption of each node by replacing dying CHs or nodes and adjusting partial MST within a cluster without re-configuration. In other words, CEMST only fixes the routes disconnected due to these dying nodes. The rest of this paper is organized as follows. Section 2 reviews the relevant literature. Section 3 introduces the proposed intra- and intercluster methods. In Section 4, we study the effectiveness of the model parameters and perform simulations to compare the performance of CEMST against other methods. Section 5 presents conclusions.
Fig. 2. Overlapped sensing area of friends of sensor 𝑖.
Hybrid energy-efficient distributed clustering (HEED) approach [29] is similar to LEACH, with CHs chosen in reference to 𝐶𝐻𝑝𝑟𝑜𝑏 = 𝐶𝑝𝑟𝑜𝑏 ×
(2)
where 𝐸𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 and 𝐸𝑚𝑎𝑥 respectively denote the current residual energy and initial energy capacity, and 𝐶𝑝𝑟𝑜𝑏 denotes the expected ratio of CHs to nodes in the network. When the clustering process is triggered, the sensor nodes broadcast their probability to their neighbors and bid for cluster CHs. Other sensors choose their CHs by evaluating the communication cost and number of neighbors. Unequal cluster-based routing (UCR) [13] applies a multi-hop protocol to inter-cluster communications. UCR resolves a hot-spot problem by dividing the network into clusters with different size depending on their distance to the BS. The range of cluster 𝑖 is controlled by 𝑅𝑖 = (1 − 𝑐
𝑑𝑚𝑎𝑥 − 𝑑(𝑠𝑖 , 𝐵𝑆) )𝑅0 𝑑𝑚𝑎𝑥 − 𝑑𝑚𝑖𝑛
(3)
where 𝑅0 and (1 − 𝑐)𝑅0 respectively denote the maximum and minimum cluster range. A constraint c is between 0 and 1, 𝑑(𝑠𝑖 , 𝐵𝑆) denotes the distance between CHs 𝑠𝑖 and BS. Notations 𝑑𝑚𝑎𝑥 and 𝑑𝑚𝑖𝑛 respectively denote the maximum and minimum distance of sensors to the BS. Abdulla et al. propose a hybrid of flat and hierarchical multi-hop routing algorithms to improve the lifetime of sensor networks [13]. Flat multi-hop routing is regarded as a chain-based protocol, and LEACH is a well-known hierarchical multi-hop routing protocol. The hybrid method also considers a network’s hot-spot area, and improves overall network lifetime. The nodes in the hot spot area [13] apply flat multi-hop routing to reduce power consumption due to data fusion. The clusters outside the hot spot area apply hierarchical multi-hop routing to save power from distant transmissions. Flow-balanced routing (FBR) [15,16] is a multi-hop inter-cluster protocol used to achieve power efficiency and coverage preservation. It defines a set 𝐹𝑖 of friends of node 𝑖 in which their sensor areas intersect with that of node 𝑖. FBR takes the sensing area intersection into account and proposes a notion of overlapping degree 1 ⋃ 𝜌𝑖 = 𝐴 ∩ 𝐴𝑗,𝑘 (4) 𝐴𝑖 𝑗,𝑘∈𝐹 𝑖
2. Literature review Low energy adaptive clustering hierarchy (LEACH) [28] is a typical cluster-based protocol for sensor networks. LEACH is one-hop for both inter- and intra-cluster communication. In the setup phase of each round, each node generates a random variable between 0 and 1. If the number is less than a threshold value 𝑇 (𝑛) defined in Eq. (1), the node is chosen as a CH for the current round. ⎧ 𝑘 , 𝐶𝑖 (𝑡) = 1 ⎪ 𝑇 (𝑛) = ⎨ 𝑁 − 𝑘[𝑟 𝑚𝑜𝑑 (𝑁∕𝑘)] ⎪ 𝐶𝑖 (𝑡) = 0. ⎩0,
𝐸𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 𝐸𝑚𝑎𝑥
(1)
In Eq. (1), k is the expected number of CHs, N is the number of nodes in the network and r is the current round. Function 𝐶𝑖 (𝑡) returns 0 if node 𝑖 has already been a CH and 1 otherwise. All nodes will have been CHs after N/k rounds. The sensor nodes choose their CHs by evaluating the received broadcasting message consisting of the CH’s ID and radio signal strength index (RSSI) from the potential CHs.
𝑖
where 𝐴𝑖 denotes the sensor area of sensor 𝑖 and 𝐴𝑖 ∩ 𝐴𝑗 denotes the overlapping area with adjacent sensing area. Sensors 𝑗 and k denote the 16
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energy, distance to the BS and duration of current CHs. In general, sensors are usually deployed randomly in a plane such that the clusters with loosely deployed member nodes consume much more energy than those with densely deployed nodes. When two clusters have the same distance to the BS, a denser cluster can have a smaller cluster range than that of loose cluster. When two clusters have the same density, the cluster with the longer distance to the BS has the longer cluster range. DBCSA applies a function ⌊ ⌋ 𝑅𝑒 (𝑢) − 𝛾 × 𝐻 (𝑢) , 𝑊 (𝑢) = 𝜙 × 𝐷𝑘 (𝑢) + 𝜑 × 𝐸 (𝑢) 0 ≤ 𝜙, 𝜑, 𝛾 ≤ 1, 𝛾 < 𝜙 + 𝜑 < 1
(5)
to determine whether node u becomes a CH. 𝐷𝑘 (𝑢) is the distance of node u to BS, 𝐷𝑘 (𝑢) is the number of links to other nodes, 𝑅𝑒 (𝑢) is the residual energy of u, 𝐸 (𝑢) is the initial energy and 𝐻 (𝑢) is the duration of the current CH. Three coefficients 𝜙, 𝜑 and 𝛾 are used to adapt to the network diversity. 3. Proposed methods CEMST is composed of three phases: cluster formation, route construction and re-construction. Cluster formation determines the CHs and their members in terms of residual energy and the degree of overlap amongst members. It also determines cluster size in accordance with residual energy, node density and average distance to the BS. In route construction, the intra-cluster routes are constructed using the selfstabilizing algorithm while the inter-cluster routes are constructed using the Borůvka-MST algorithm. The re-construction phase is activated when the CHs die or the intra-cluster links are disconnected due to sensor death. To save energy, it re-connects the disconnected nodes and replaces the dying CHs without cluster re-configuration.
Fig. 3. Example of sensor density.
friends of node 𝑖, i.e., 𝑗, 𝑘 ∈ 𝐹𝑖 . FBR constructs a hierarchical backbone in a graph structure amongst CHs such that they can transmit data in parallel. When the CHs on the backbone die, adjacent nodes can repair the disconnected routes. Balanced clustering algorithm with distributed self-organization (DSBCA) [30] is a multi-hop protocol for both inter- and intra-cluster communication. Its CH selection considers the node density, residual
Fig. 4. An example of cluster formation.
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Fig. 5. Cluster_formation algorithm.
3.1. System model and assumption
3.2. Cluster formation
Before presenting our work, some assumptions and notations are addressed.
Cluster formation is performed in two stages during network initialization. The first stage selects the CHs in accordance with the overlapped sensing area, node density and residual energy. When a CH is located in a high-overlap sensing area, its death does not significantly affect data acquisition quality. The second stage determines the range of each cluster according to its distance to the BS and node density. Let d and r respectively denote the transmission distance and sensing range, and 𝑑 = 𝑟 ⋅ 𝑘 where 𝑘 ≥ 2.
• • • •
A BS is stationary and located at the outer sensing area. Sensors are stationary after deployment. Sensors are identical and labeled with unique identifications (IDs) Each sensor has two modes: active and dormant. In the dormant mode the energy consumption for sensing and communication is ignored. • Transmission power/range can be controlled by sensors, and the distances can be approximated by RSSI measurement given a known transmission power. • Clustering is performed once at network initialization.
Definition 1. Sensors 𝑖 and 𝑗 are friends if there is an overlapping sensing coverage between them. Definition 2. Sensor 𝑗 is a neighbor of sensor 𝑖 if 𝑗 is in the transmission range.
Corresponding to the components in Fig. 1, the RF power consumption model [5,30,31] is defined as { 𝑙𝐸𝑒𝑙𝑒𝑐 + 𝑙𝜀𝑓 𝑠 𝑑 2 , 𝑑 < 𝑑0 𝐸𝑇 𝑥 (𝑙, 𝑑) = 𝐸𝑇 𝑥−𝑒𝑙𝑒𝑐 (𝑙) + 𝐸𝑇 𝑥−𝑎𝑚𝑝 (𝑙, 𝑑) = 𝑙𝐸𝑒𝑙𝑒𝑐 + 𝑙𝜀𝑚𝑝 𝑑 4 , 𝑑 ≥ 𝑑0
Notably, a sensor with many neighbors does not necessarily have many friends. An example of an overlapping sensor area of sensor 𝑖 is shown in Fig. 2. The ratio of overlapped sensing area 𝜌𝑖 is defined as
(6)
𝜌𝑖 =
where 𝑙 is the length of the transmission √data in bits, and d denotes the distance to the receiver. Notation 𝑑0 =
𝜀𝑓 𝑠
𝜀𝑚𝑝
𝐴𝑖
, 1 > 𝜌𝑖 > 0,
(8)
where 𝐴𝐹𝑖 denotes the sensing area of the friends of sensor 𝑖, and 𝐴𝑖 denotes the sensing area of sensor 𝑖. Assuming 𝑘 = 2 and all sensors are randomly deployed in a plane according to uniform distribution, an adequate density for sensor 𝑖 is
denotes reference distance
where 𝜀𝑓 𝑠 and 𝜀𝑚𝑝 are respectively the amplifier power required in free space and multi path propagation. The power loss of both the free space (𝑑 2 ) and multi-path fading (𝑑 4 ) channel model are based on the distance between the transceivers. When a node receives 𝑙 bits of data, it requires energy 𝐸𝑅𝑥 (𝑙) = 𝐸𝑅𝑥−𝑒𝑙𝑒𝑐 (𝑙) = 𝑙𝐸𝑒𝑙𝑒𝑐 .
𝐴𝑖 ∩ 𝐴𝐹𝑖
𝐶 (𝑖) =
|𝐹 (𝑖)| ≅ 0.25, |𝑁(𝑖)|
(9)
where |𝐹 (𝑖)| and |𝑁(𝑖)| respectively denote the number of friends and neighbors of sensor 𝑖. Given 𝑑 = 𝑟 × 𝑘 and 𝑘 = 2, the transmission
(7) 18
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density 𝐶 (𝑖) denotes the ratio of friends to the neighbors in a sensor’s transmission range. The notion of density 𝐶 (𝑖) is used to balance the power consumption amongst a node and its neighbors, while 𝜌𝑖 is used to ease the quality degradation of data acquisition from its friends. Since CHs require additional power for collecting and processing the sampling data from its members, the percentage of residual energy is ⌊ ⌋ 𝐸𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 (𝑖) 𝐸 (𝑖) = × 100%, (10) 𝐸𝑚𝑎𝑥 (𝑖) where 𝐸𝑟𝑒𝑠𝑖𝑑𝑢𝑎𝑙 (𝑖) and 𝐸𝑚𝑎𝑥 (𝑖) respectively denote the residual and initial energy of node 𝑖. In accordance with Eqs. (8)–(10), a function is derived as Fig. 6. Intra_self-stabilizing algorithm.
𝑊 (𝑖) = 𝜌𝑖 × 𝛼 + (𝐶 (𝑖) − 0.25) × 𝛽 + 𝐸 (𝑖) × 𝛾,
0.1 ≤ 𝛼, 𝛽, 𝛾 ≤ 1
(11)
where coefficients 𝛼, 𝛽 and 𝛾 are used to control the proportion of each item. The notations In Eq. (11) are regarded as three parts which can be tuned in terms of different requirements using 𝛼, 𝛽 and 𝛾. In the first part 𝜌𝑖 ×𝛼, 𝜌𝑖 presents the ratio of overlapped sensing area to the sensing area of node 𝑖. Coefficient 𝛼 controls the importance of 𝜌𝑖 in 𝑊 (𝑖). When 𝜌𝑖 is high, the nodes may be densely deployed in their sensing area and more CHs can be elected without influence of sampling quality. Additionally, if 𝜌𝑖 is higher than a threshold 𝑇𝑑𝑒𝑛𝑠𝑒 , node 𝑗 is switch to dormant mode for saving mover energy and prolonging network lifetime. Those CHs are applied to process and forward data, shorten the transmission range of sensors and balance the load/lifetime of the sensors. In the second part, (𝐶 (𝑖) − 0.25) × 𝛽 considers the density of friend and neighbor nodes in the transmission areas of node i. This part is used to balance the power consumption among its neighbors, and is tuned by 𝛽. The notation 𝐸 (𝑖) in the third part introduced widely by many clustering algorithms is controlled by 𝛾 in the CEMST.
area of a node is approximately four times the size of its sensing area shown in Fig. 3. When the sensor nodes are deployed on the area in uniform distribution, the transmission range covers about four times of nodes as those in the sensing area. In other words, |𝑁(𝑖)| must be around four times larger than |𝐹 (𝑖)| so as to correspond to the assumed node distribution. If the adequate density ratio does not follow the distribution, say 𝐶 (𝑖) = 0.5, system would overestimate the required number of sensors. In the area, none of the sensors can switch to dormant mode when actual density is about 0.5, resulting in over sampling and energy inefficiency. In case sensor deployment applies other distribution, the model of 𝐶 (𝑖) may need to redesign. However, a deployment with non-uniformed distribution may not suitable for practical use. Fig. 3(a) shows the case of 𝐶 (𝑖) ≅ 0.25, where the transmission area is about four times the sensing area, as is the number of nodes. The cases 𝐶 (𝑖) ≪ 0.25 and 𝐶 (𝑖) ≫ 0.25 respectively shown in Fig. 3(b) and (c), hamper a cluster’s energy balance. In Fig. 8(b), node 𝑖 will exhaust its battery power faster than its neighbors because it must process and forward data from them. Similarly in Fig. 8(c), node 𝑖 would consume more energy than its friends. Different from 𝜌𝑖 ,
The cluster range is determined by ⌊ ⌋ 𝐷 (𝑖) 𝑅𝑖 = 𝜑 × , 𝐶 (𝑖)
Fig. 7. An example of the intra_self-stabilizing algorithm.
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(12)
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Fig. 8. Example of MST construction among CHs.
Fig. 9. Inter-cluster_MST algorithm.
Fig. 11. Example of CH replacement.
Fig. 10. Example of inter-cluster labeling.
Fig. 12. CH_replacement algorithm.
where 𝜑 ≥ 2𝑟 prevents 𝑅𝑖 from being less than sensor range 𝑟. Notation
𝐷 (𝑖) = (1 −
𝑑𝑖 ) 𝑑𝑚𝑎𝑥
(13)
members, that is ⌊ ⌋ 𝐷 (𝑖) 𝑅𝑖 = 𝜑 × ≥ 𝑇 ℎ. 𝐶 (𝑖)
denotes the distance function of node 𝑖 to BS and affects the size of a cluster where 𝑑𝑖 is the distance of CH i to BS. Notation 𝑑𝑚𝑎𝑥 is the maximum distance between a node and BS in the network. When a CH 𝑖 is far away from BS, the value of D(𝑖) could be very small and result in a larger cluster. In other words, the CEMST reduces the cluster range as the node density increases or the cluster is adjacent to BS. Let 𝑇 ℎ be an upperbound of 𝑅𝑖 such that the CHs are not isolated or do not lack for
The upper-bound of the distance to BS is derived as ) ⌋ ⌊( 𝐶 (𝑖) 𝑑𝑖 ≤ 1 − 𝑇ℎ × 𝑑𝑚𝑎𝑥 . 𝜑
(14)
(15)
If a node with high value of 𝑊 (𝑖) does not satisfy Eq. (15), it cannot be chosen as a CH because it may be located far away. Algorithm 20
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Fig. 14. Route_re-construction Algorithm.
cluster_formation is shown in Fig. 5 and an example of the algorithm is depicted in Fig. 4. In Fig. 4(a), all sensor nodes performed by initialization phase receive a signal CLUSTER_FORM (t ) with in a time constraint t from BS and try to find their friends and neighbors. In accordance with the surrounding information, they compute the value derived from the function W (𝑖) in Eq. (11). A countdown timer starting to the time constraint limits the initial clustering duration. In the phase of State_Determination, each node bids for CH (lines 2–4) when they have the highest weighting value among their neighbors and satisfy the distance constraint to BS in Eq. (15). An example for node 𝑖 is shown in Fig. 4(b), each node also determines their friends and neighbors. In line 5, each CH updates its transmission range and broadcasts a HEAD signal to its neighbors and exit the phase. An example is shown in Fig. 4(c). In lines 6–7, other non-CH nodes receiving a HEAD signal become the members of the signal source. When a node 𝑗 with 𝜌𝑗 higher than density threshold 𝑇𝑑𝑒𝑛𝑠𝑒 , it will be switched to the sleep mode. In
Fig. 13. Example of node reconnection.
Fig. 15. Clustering structure with 200 nodes.
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Fig. 16. Clustering structure with 300 nodes.
lines 8–9, the isolated nodes join the CH with the shortest distance to them. Finally, CH 𝑖 reports the clustering results to BS, and a cluster is formed. The example is shown in Fig. 4(d). Notably, Algorithm 1 does not construct transmission paths. The path construction is performed by self-stabilizing algorithm.
We define a connected graph G = (V, E) which consists of a set V of CHs, and a set E of links labeled with a distance between two CHs. To derive the shortest paths among clusters, we construct a MST based on Borůvka’s Algorithm as follows. 1. Initially, each node denotes a tree T. 2. Each tree T finds the shortest edge to other trees, which does not cause any loop. 3. Combine the trees to form a bigger tree. 4. Repeat steps 2 and 3 until an MST of G is produced.
3.3. Route construction This section presents intra- and inter-cluster communication methods. A self-stabilizing algorithm is used to construct the shortest paths within a cluster, which does not need network initialization and provides fault-tolerance that satisfies the independent operation of individual clusters. Let G = (V, E) be a connected graph and represent a cluster. E and V respectively denote a set of all sensors and edges, where {∀{𝑖, 𝑗} ∈ 𝐸|𝑖, 𝑗 ∈ 𝑉 and 𝑖 ≠ 𝑗}. Notation d(𝑖) of sensor 𝑖 records the length of the shortest path, r denotes a CH, M denotes a set of sensor nodes in the cluster, and S denotes a set of nodes which already derive their shortest paths. The proposed self-stabilizing algorithm shown in Fig. 6 is similar to Dijkstra’s algorithm [19], which constructs the singlesource shortest-path in a cluster. The example of self-stabilizing algorithm is presented in Fig. 7. Node 5 is true for R0, nodes 2 and 3 are also true for R1. In Fig. 7(b), node 6 is true for R1 such that 𝑑(6) = 15. In Fig. 7(c), node 7 is true for R1 and has the two shortest paths. It selects the link to the neighbor with the maximum residual energy, and therefore node 7 derives 𝑑(7) = 20. Finally, in Fig. 7(d), the nodes in a cluster reach a steady state and derive their shortest paths.
In Fig. 8(a), for example, CHs broadcast a message to measure the distance to its neighbors. By repeating stages 2 and 3, the subtrees are emerged in Fig. 8(b). In Fig. 8(c), the subtrees can be combined using edge {e, b} with the minimum distance between the two trees. Finally, an MST can be constructed amongst CHs and BS. Fig. 9 presents the inter-cluster_MST algorithm. In line 1 of the algorithm, each CH is initially regarded as a component in a set T. A while-loop in lines 2–7 is applied to process the components in T until only one component is left. In lines 3–6, each component is drawn from T and records its edge with the minimum weight using a set S, where the edges must be connected to the CHs in other components. After adding an edge to S, its two connected components are combined as one component. Finally, an inter-cluster MST is constructed using the edges in S. To forward the sensor data to the BS, a MST is rooted in the BS and each CH is labeled in a hierarchical order shown in Fig. 10. The BS labeled with P(0) is at level 0. In level 2, CHs are labeled in increasing order from left to right and so on for the next level. 22
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Fig. 17. Clustering structure with 400 nodes.
Fig. 18. Effectiveness of 𝜌𝑖 .
Fig. 19. Effectiveness of 𝐶 (𝑖).
3.4. Intra-cluster re-routing algorithms to the cluster members and perform algorithm Intra_self-stabilizing in Fig. 6 to construct the new routes. After algorithm CH_replacement is accomplished, the old CH is recovered to a member sensor. The result is shown in Fig. 11(d), and algorithm CH_replacement is shown in Fig. 12. When the residual energy 𝑅𝑒 (𝑗) of CH 𝑖 is lower than a threshold, algorithm CH_replacement is performed to elect a new CH. In line 1, a CH broadcasts a HELP signal to its cluster members, and they will re-compute their values of W (𝑗) according Eq. (11). In line 3, a new CH is elected in terms of W (𝑗) and broadcasts a HEAD signal to the
As mentioned earlier, the proposed method does not require cluster re-configuration as CHs die. The CH_replacement algorithm is applied to replace dying CHs, and route_re-construction algorithm is used to repair routes disconnected due to dead nodes. In Fig. 11(a), CH_replacement is initiated when the residual energy 𝑅𝑒 (𝑗) of a CH less than a threshold 𝑟𝑡ℎ . In Fig. 11(b), the dying CH broadcasts a HELP message to all cluster members, including dormant nodes. In Fig. 11(c), each node 𝑖 computes its W (𝑖), and, excluding dying CHs, the node with the maximum value of W (𝑖) is selected as a new CH. The new CH broadcasts a HEAD message 23
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Fig. 20. Lifetime of CEMST, DSBCA and FBR with 𝛼 = 0.5 𝛽 = 0.4 and # nodes = 200.
Fig. 22. Lifetime of CEMST, DSBCA and FBR with 𝛼 = 0.5 𝛽 = 0.4 and #nodes = 400.
Fig. 21. Lifetime of CEMST, DSBCA and FBR with 𝛼 = 0.5 𝛽 = 0.4 and #nodes = 300.
Fig. 23. Coverages of CEMST, DSBCA and FBR with 𝛼 = 0.5, 𝛽 = 0.4 and #nodes = 200.
other cluster members. The former CH 𝑖 then invokes algorithm route_reconstruction to adjust the path to its adjacent nodes. When a node is almost running out of energy, route_re-construction algorithm is invoked by the node to broadcast a DEATH message to its friends. The friends calculate their respective W (𝑖) values and a friend with the maximum W (𝑖) value becomes a connector. The other friends will reconnect to this connector and proceed with their sensor tasks. Notably, a connector is a sensor member and cannot be the current CH. In the example shown in Fig. 13(a) and (b), a node 𝑗 is running out energy and broadcasts a DEATH message to its friends k, l and 𝑖. In Fig. 13(c), k, l and 𝑖 calculate their W (𝑖) values according to Eq. (11), and node 𝑖 is chosen as a connector. Finally, in Fig. 13(d), friends k and l change their connections to node 𝑖. Fig. 14 shows the route_re-construction algorithm. In line 1, a dying CH 𝑗 broadcasts a DEATH signal to its adjacent nodes, i.e., the directly connected nodes. In line 2–4, all the adjacent nodes re-compute their values of W (𝑗) according Eq. (11) and connect to their adjacent nodes with the highest value of W (k), 𝑘 ≠ 𝑗. Finally, in line 5, nodes k are notified to connect node 𝑗. In a multi-hop cluster, the nodes in the cluster border rely on their neighbors to forward their data to CH. When a CH is dead, its member nodes do not disappear or dismiss on their existing MST path due to selfstabilizing feature. The disconnected MST path can still work because a new CH can be elected by CH_replacement algorithm and the MST can be re-connected by route_re-construction algorithm. CEMST has already constructed the energy efficient clusters and MST paths during initial phase, assuming the network is composed of identical nodes uniformly deployed in the target area. Therefore, the nodes in the border switching to another cluster do not improve average energy efficiency of the network even if the number of dead nodes increases resulting a sparse network.
Fig. 24. Coverages of CEMST, DSBCA and FBR with 𝛼 = 0.5, 𝛽 = 0.4 and #nodes = 300.
Fig. 25. Coverages of CEMST, DSBCA and FBR with 𝛼 = 0.5, 𝛽 = 0.4 and #nodes = 400.
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Fig. 26. Relationship between lifetime and cluster range with #nodes = 200.
4. Performance evaluation
4.1. Experimental assumptions and settings The simulations are developed using MATLAB. The parameter values used in the simulations are shown in Table 1, and the performance metrics are examined with various coefficients mentioned in Section 3. A network scenario with a 100 × 100 m2 sensor area is used to simulate 200, 300 and 400 sensor nodes randomly deployed in the sensor area. The sink node, i.e. the BS, is located at 120 m, 50 m with an infinite power supply, and all sensors have an initial energy of 0.5 J. Experiment durations are performed in units of round similar to [16,30]. At the beginning of each round, FBR and CEMST do not require cluster reconfiguration. FBR must perform routing algorithm for each packet, and CEMST only performs CH_replacement and route_re-construction for a cluster when a node dies.
In this section, we evaluate the performances of CEMST, FBR and DSBCA and derive the effectiveness of the parameters applied by the proposed method. Experimental results provide suitable parameter settings for the proposed method. The experiments measure: (1) Parameter effectiveness: We simulate the target methods in terms of cluster ranges, i.e.,
𝑅(𝑖) , 2𝑟
node density C(𝑖) and degree of
overlap 𝜌𝑖 . (2) Performance: We compare clustering structures produced by CEMST, DSBCA and BFR in different network sizes. The effectiveness of the cluster range and network lifetime are discussed
4.2. Clustering structure
when the first, 10%, 20%, 30% and 40% sensors die. Also, the coverage ratio of the target methods are also compared in terms
Figs. 15–17 respectively show simulations of cluster structures with 200, 300 and 400 nodes. The clustering structure of CEMST is relatively
of network lifetime. 25
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Fig. 26. (continued). Table 1 Parameters used in simulations. Parameters
Values
Sensor area BS location Number of sensor nodes Initial energy of sensor nodes Packet size
100 m × 100 m (120, 50) 200, 300, 400 0.5 J 2000 bits 𝑇 𝑟𝑎𝑛𝑠𝑚𝑖𝑡 ∶ { 𝑙𝐸𝑒𝑙𝑒𝑐 + 𝑙𝜀𝑓 𝑠 𝑑 2 , 𝑑 < 𝑑0 𝐸𝑇 𝑥 (𝑙, 𝑑) = 𝑙𝐸𝑒𝑙𝑒𝑐 + 𝑙𝜀𝑚𝑝 𝑑 4 , 𝑑 ≥ 𝑑0
Energy consumption model
a CH consumes more energy for fusion and forwarding the data from its neighbor nodes. The maximum distance between CHs and BS (max_dist ) dominates the network latencies because CHs transmit a large amount of data through a longer distance than an intra-cluster communication. In Fig. 15, the networks with 200 nodes are simulated using FBR, DSBCA and CEMST. CEMST produces the smallest number of cluster (#cluster = 19) and has the smallest maximum CH degree of all other methods (max_deg = 4). The example with spars nodes shows that CEMST can achieve better energy efficiency than other methods. In the example, CEMST requires up to 9 hops (max_dist = 9) from a CH to BS and outperforms other methods in transmission latency. Comparing Fig. 16(c) with Fig. 16(a) and (b), CEMST also requires the fewest clusters (#cluster = 22) and produces the smallest max_deg (= 4) of CHs amongst all other methods. In addition, CEMST has the shortest maximum inter-cluster routes (max_dist = 9) than FBR and DSBCA thus improving energy efficiency and network lifetime. In the high density node deployments shown in Figs. 16 and 17, CEMST more obviously outperforms other methods in energy efficiency because the values of #cluster (= 22 and 24) are slowly increased along with node density. Also, the values of max_deg (= 4) of CHs is unchanged which benefit the management of cluster members. Compare to other methods, their #cluster and max_deg increase rapidly with the node density, which their energy efficiency. In the examples, CEMST also produces the most stable max_dist (= 9 and 10) and therefore the lowest intercluster transmission latency. The cluster decision of DSBCA is based on residual energy and link density, which is highly dependent on sensor deployment. Therefore, the number of clusters may increase due to uneven sensor deployment. In CEMST, the CHs are determined by a weighting function considering sensor overlapping ratio, node density and residual energy. The range of clusters also considers their distance to the BS. The inter-cluster routes amongst the CHs are constructed using the MST algorithm, and thus the number of CHs is controlled by sensor coverage and energy efficiency. To prevent ‘‘Hot Spot’’ effect, Eq. (11) applies C(𝑖) to determine the number of CHs in an area with
𝑅𝑒𝑐𝑒𝑖𝑣𝑒 ∶ Transmit, receiving energy consumption per bit 𝐸𝑒𝑙𝑒𝑐 Amplifier power consumption 𝜀𝑓 𝑠 (𝑑 < 𝑑0 ) Amplifier energy consumption 𝜀𝑚𝑝 (𝑑 ≥ 𝑑0 ) Data compression ratio Energy threshold ratio (𝑟𝑡ℎ ) Data fusion energy (EDA)
𝐸𝑅𝑥 (𝑙) = 𝑙𝐸𝑒𝑙𝑒𝑐 50 nJ/bit 10 pJ/bit//m2 0.0013 pJ/bit/m4 0.7 0.3 5 nJ/bit
better balanced. We can see that FBR applies single-hop transmission to the intra-cluster, resulting in significant CH overhead and requiring additional backup CHs, thus reducing sensing accuracy and energyefficiency. DSBCA and CEMST apply multi-hop to intra-cluster communications, and construct tree-based structures for inter-cluster routing. We discuss three metrics #clusters, max_deg and max_dist to evaluate the performance of energy efficiency, balance and latency of underlying cluster structures. A network with a number of clusters (#cluster) requires #cluster CHs that spend additional energy to manage their cluster members and forward data, which disable their sensing tasks and hamper their service and energy balance. The maximum degree of a CH (max_deg ) defines the largest number of sensors that directly connect to the CH in a network. Along with a higher value of max_deg, 26
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Fig. 27. Relationship between lifetime and cluster range with #nodes = 300.
uneven nodes so as to balance the power consumption of CHs. R(𝑖) helps
4.3. Effectiveness of the cluster parameters
determine the range of the clusters in terms of their distances to BS. For In accordance with function 𝑊 (𝑖) in Eq. (11), coefficients 𝛼 and 𝛽 are used to control the impact of the parameters 𝜌𝑖 and 𝐶 (𝑖) and examine their effectiveness in the networks with 100 sensor nodes. Each combination of parameters is performed 100 times. It is known that 𝐸 (𝑖) directly affects sensor lifetime and thus its initial value is 1 in the simulations. Figs. 18 and 19 present the effectiveness of 𝜌𝑖 and 𝐶 (𝑖), respectively. On average, Fig. 18 presents that a low value of 𝛼 would decrease communication rounds by 5% when 40% nodes die comparing to high value of 𝛼. When the first node dies, a low value of 𝛼 would decrease communication rounds by an average of 14%. We can conclude that, when the sampling quality is not prioritized, low sensor coverage would benefit network lifetime. In other words, we can trade the network lifetime with sampling quality in the high percentage of dead sensors. On the contrary, Fig. 19 shows that 𝛽 controlling 𝐶 (𝑖) has insignificant effectiveness on the number of dead nodes. The main reason is that, Algorithm 1 would switch the nodes locating in high density of sensing coverage to dormant mode. Therefore, the sensor
example, in Fig. 15(c), the node in position x = 90, y = 35 is belong to the CH in position x = 80, y = 19. However, there are CHs closer, like the one in x = 80, y = 40. The reason is that the CHs in position x = 80, y = 39 and x = 86, y = 52 are located among densely nodes. In addition, they are close to BS than the CH in position x = 80, y = 19. In accordance with the proposed C(𝑖) and R(𝑖), their ranges and the number of members should be smaller than the CH in position x = 80, y = 19. Therefore, the node in position x = 90, y = 35 is excluded by those two CHs. Other situation such as the node in position x-42, y = 62 has a CH next to it. CEMST regards data transmission range without data receiving precedence due to self-stabilizing algorithm. The sensor node in x = 42, y = 62 may receive HEAD signals from the CH in x = 55, y = 75 earlier than that from the CH of x = 40, y = 62, and it becomes the member of the CH in x = 55, y = 75. 27
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Fig. 27. (continued).
coverage in CEMST should be uniformed and does not required to be changed during the network operation time to simplify the sensor and software design.
4.5. Effectiveness of cluster coverage This section discusses the cluster coverage ratio of FBR, DSBCA and CEMST in the range of 100–700 rounds. The simulations are performed with 200, 300 and 400 nodes deployed in the 100 × 100 m2 sensing area in Figs. 23–25, respectively. The results show that CEMST produces higher cluster coverage ratio than those of FBR and DSBCA. FBR is a coverage-aware protocol and slightly outperforms the multi-hop protocol DSBCA in a large-scale network (#nodes = 400). In a largescale network, randomly deployed nodes may overlapped their sensing areas in a high proportion. A node with high proportion overlapped area is assigned as a CH that balances high transmission load of other CHs. The CH does not degrade cluster coverage and sampling quality while balancing the nodes lifetime. Additionally, some of the nodes with high values of 𝐶 (𝑖) and 𝜌𝑖 can be put into dormant mode to reduce energy consumption and cannot affect the coverage ratio. To compare Figs. 23–25 respectively with Figs. 26–28 corresponding to the same communication rounds, we also have the following observations. In Fig. 23, CEMST achieves about 50% coverage in 40% dead nodes among 200 nodes. In contrast, FBR and DSBCA have less 20% coverage when there are 40% of dead nodes. In Fig. 24, CEMST achieves about 70% coverage in 40% dead nodes among 200 nodes, and the coverages achieved by FBR and DSBCA are less than 60%. Corresponding Fig. 25 with Fig. 22, CEMST reaches 87% coverage when 40% nodes die and outperforms the coverages of FBR and DSBCA.
4.4. Network lifetime In this section, the network lifetime using FBR, DSBCA and CEMST are evaluated when the first, 10%, 20%, 30% and 40% of nodes die. We simulate network operations in 200, 300 and 400 nodes deployed in a 100 × 100 m2 sensor area. Each combination of parameters 𝛼 and 𝛽 is performed in experiments 50 times to calculate the average number of communication rounds. In Fig. 20, we simulate 200 sensor nodes with coefficients 𝛼 = 0.1 and 𝛽 = 0.4, finding that CEMST outperforms FBR and DSBCA for all proportions of dead nodes. DSBCA outperforms FBR when the proportion of dead nodes is less than or equal to 20%, because DSBCA considers the link density. In other words, when a CH locates in low-density region, the multi-hop cluster range should be expanded. As the number of dead sensors increases (e.g., 30%), the existing nodes have to consume more power to transmit data to their CHs. Meanwhile, in FBR, the nodes in the one-hop cluster do not spend more power to transmit data to their CHs, and therefore FBR outperforms DSBCA when the percentage of dead nodes exceeds 20%. CEMST is a multi-hop clustering approach and can change partial routes in a cluster when a node dies without cluster re-configuration, and therefore it outperforms FBR and DSBCA. Figs. 21 and 22 show the average rounds of communication in 300 and 400 nodes, respectively. For the same rounds, CEMST has the lowest ratio of dead nodes compared to the other methods, and the same conclusion follows. Compared with Fig. 20, DSBCA outperforms FBR in terms of communication rounds when the percentage of dead nodes is less than or equal to 30% and 40%, respectively, in Figs. 21 and 22. We can conclude that one-hop cluster communication hampers network lifetime given a large number of nodes in the network. In Figs. 20–22, the differences amongst the underlying methods in terms of the average number of rounds decreases as the network size increases.
4.6. Effectiveness of cluster range Cluster range is an important factor in determining WSN network lifetime. We study how maximum cluster range affects the performance of the target methods in a 100 × 100 m2 area. Figs. 26–28 show the average number of communication rounds for the various methods for 50 experiments with different maximum cluster ranges. The range of control parameters are 𝛼 = [0.1, 1.0], 𝛽 = [0.1, 1.0] and 𝛾 = 1. According 28
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Fig. 28. Relationship between lifetime and cluster range with #nodes = 400.
to Eq. (14), the cluster range RiMax in terms of sensor radius r changes from 2r to 7r and 𝑇 ℎ = 7𝑟. The cluster ranges are not controlled by FBR, while those in DSBCA and CEMST are controlled by the distance to the BS and respectively by link density and node density. Although the node density defined in Eq. (9) is different from the link density, they are both designed to resolve hot-spot problem and prolong network lifetime. Fig. 26(a)–(e) respectively present the experimental results for 200 nodes with the death of the first node along with the deaths of 10%, 20%, 30% and 40% of all nodes. When the first node dies, FBR does not change with cluster range and has the lowest lifetime. The lifetime of CEMST is longer than those of the other methods and increases with cluster range while the trend of DSBCA is not obvious. In Fig. 26(b)–(e), the network lifetimes of all methods increase with cluster ranges, and FBR increases more rapidly with the percentage of dead nodes. Fig. 27(a)–(e) simulate 300 nodes in a 100 × 100 m2 area and show the average lifetime. Both DSBCA and FBR are not sensitive to the cluster ranges when the first node dies, and CEMST still outperforms other methods in all cluster ranges. Except for the first node death presented in Fig. 27(a), FBR still increases the most rapidly of all methods. It
outperforms DSBCA when 40% nodes die and the cluster range exceeds 5r. Compared with Fig. 26, the growth of the lifetime in Fig. 27 is relatively slow as cluster range increases. Fig. 28(a)–(e) simulate a network with 400 nodes. FBR is worse than other methods for all cluster ranges. Figs. 26–28 show that CEMST achieves the best performance under various conditions, and follow a similar trend as that shown in Fig. 27. In the worst case, Fig. 28(e) shows that CEMST incurs 403 and 592 communication rounds when 40% of nodes die, but this is still better than the other two methods. In Figs. 26–28, as increasing the number of dead nodes results in small performance differences among the underlying methods. 5. Conclusions This paper proposes a coverage- and energy-aware method with minimum spanning trees (CEMST) for multi-hop cluster WSNs. In CEMST, cluster size is elaborated with the complete experiments, and the effectiveness of the proposed parameters such as degree of sensor overlap, node density are examined. The distance to the BS is employed 29
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Fig. 28. (continued).
to balance node energy consumption using self-organizing algorithms. Compared with the existing methods, CEMST constructs more suitable cluster structures and energy-efficient routes. When a node or CH dies, CEMST adjusts the local routes of the cluster and replaces the CH without re-configuration, thus extending network lifetime. Simulation results show that CEMST outperforms other methods in cluster coverages and network life time, and is scalable for networks with different cluster sizes to achieve energy efficiency.
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