A real-time routing protocol with load distribution in wireless sensor networks

Computer Communications 31 (2008) 3190–3203

Contents lists available at ScienceDirect

Computer Communications journal homepage: www.elsevier.com/locate/comcom

A real-time routing protocol with load distribution in wireless sensor networks Adel Ali Ahmed *, Norsheila Fisal Telecommunication Laboratory, Faculty of Electrical Engineering, University Technology Malaysia, 81310 Johor Bahru, Johor Darul Ta’zim, Malaysia

a r t i c l e

i n f o

Article history: Received 1 July 2007 Received in revised form 20 April 2008 Accepted 30 April 2008 Available online 15 May 2008 Keywords: End-to-end delay Packet reception rate Remaining power Packet velocity Delivery ratio

a b s t r a c t Wireless sensor network (WSN) is a wireless ad hoc network that consists of very large number of tiny sensor nodes communicating with each other with limited power and memory constrain. WSN demands real-time forwarding which means messages in the network are delivered according to their end-to-end deadlines (packet lifetime). This paper proposes a novel real-time routing protocol with load distribution (RTLD) that ensures high packet throughput with minimized packet overhead and prolongs the lifetime of WSN. The routing depends on optimal forwarding decision that takes into account of the link quality, packet delay time and the remaining power of next hop sensor nodes. The proposed mechanism has been successfully studied and verified through simulation and real test bed implementation. Ó 2008 Elsevier B.V. All rights reserved.

1. Introduction WSN may consist of large number of sensor nodes, which are densely deployed in close proximity to the phenomenon. In WSN, sensors gather information about the physical world and the base station or the sink node makes decision and performs appropriate actions upon the environment as depicted in Fig. 1. It is very different from traditional networks as it comprises of a large number of nodes that produce very large amount of data. However, WSNs are not free of certain constrains such as power, computational capacities, and memory. Due to these inherent properties, conventional management schemes are not efficient and effective to manage sensor networks and thus, a new management scheme is needed [1]. WSNs are very data-centric, meaning that the information that has been collected about an environment must be delivered in a timely fashion to a collecting agent or base station. Since large number of sensor nodes is deployed, neighbour nodes may be very close to each other. Hence, multi-hop routing idea is suitable for WSN to enable channel reuse in different regions of WSN and overcome some of the signal propagation effects experienced in longdistance wireless communication [1–3]. Real-time communication is necessary in many WSN applications. For example, in a fire fighting application where appropriate actions should be made in the event area immediately as delay may cause some huge damages further. The sensor data collected * Corresponding author. Tel.: +60 017 7621839. E-mail addresses: [email protected], [email protected] (A.A. Ahmed), [email protected] (N. Fisal). 0140-3664/$ - see front matter Ó 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.comcom.2008.04.030

and delivered must still be valid at the time of decision making since late delivery of data may endanger the fire fighter’s life. The general research challenges for multi-hop routing in WSN arise primarily due to the large number of constraints that must be simultaneously satisfied. One of the most important constraints on sensor nodes is the power consumption requirement. Sensor nodes carry limited, generally irreplaceable power sources. WSN applications must operate for months or years without wired power supplies and battery replaced or recharged. Therefore, the power consumption must be considered while designing multihop routing in order to prolong the WSN lifetime [4]. The real-time routing protocols have time constrain which is important to consider when designing real-time routing in WSN. This paper reports the following main contributions. Firstly, it proposes a new type of communication in WSN called geodirectioncast forwarding based on quadrant. Geodirection-cast forwarding combines geocast with directional forwarding to forward the data packet through multi-path to destination. The multi-path forwarding mechanism increases the delivery ratio of the proposed routing protocol. Secondly, it proposes a real-time with load distributed routing protocol that computes optimal forwarding node based on packet reception rate (PRR), remaining power of sensor nodes and packet velocity over one-hop. Since forwarding nodes with the best link quality are chosen, the data throughput is improved. By choosing the forwarding nodes with the maximum packet velocity, the real-time packet transfer is ensured in the WSN. Additionally, choosing nodes with the highest remaining power level ensures sporadic selection of forwarding neighbour nodes. The continuous selection of such nodes spread out the traffic load to neighbours in the direction of the sink, and subsequently

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

3191

Fig. 1. WSN with multi-hop communication.

prolonging the WSN lifetime. RTLD reports high performance in term of delivery ratio, control packet overhead and power consumption. The performance of RTLD has been successfully studied and verified through simulation and real test bed implementation. The remainder of this paper is organized as follows: Section 2 will present related work on real-time communication and power control protocols. The design of RTLD will be described in Sections 3 and 4 will describe the simulation study of RTLD. Section 5 will describe the test bed implementation. Finally Section 6 will conclude the paper. 2. Related work The behavior of routing protocol over WSN has not been addressed and evaluated by many researchers. The related research to this paper can be classified into two categories as describes as follows. 2.1. Geocast forwarding In global flooding, the sender broadcasts the packet to its neighbours. Each neighbour that does not received the packet, it broadcasts it to its neighbour. This mechanism will continue until all reachable nodes receive the packet including the geocast region nodes. It is simple but has a very high overhead and is not scalable to large networks such as WSN. GPSR [5] is a geographic routing protocol for wireless networks that works in two modes: greedy mode and perimeter mode. In greedy mode, each node forwards the packet to the neighbour closest to the destination. When greedy forwarding is not possible, the packet switches to perimeter mode, where

perimeter routing (face routing) is used to route around deadends until closer nodes to the destination are found. Ko and Vaidya [6] proposed geocasting algorithms to reduce the overhead, compared to global flooding, by restricting the forwarding zone for geocast packets. Nodes within the forwarding zone forward the geocast packet by broadcasting it to their neighbours and nodes outside the forwarding zone discard it. Each node has a localization mechanism to detect its location and to decide when it receives a packet, whether it is in the forwarding zone or not. GeoTORA [7] integrates temporally ordered routing algorithm (TORA [8]) which is used in ad hoc network with local flooding. It uses a TORA routing protocol to unicast the delivery packet to the region and then floods the packet within the region. Seada and Helmy [9] proposed two protocols for geocast: geographic-forwarding-geocast (GFG) and geographic-forwardingperimeter-geocast (GFPG). In the GFG, GPSR is used by nodes outside the region to guarantee the forwarding of the packet to the region. Nodes inside the region broadcast the packet to flood the region. GFPG uses both geocast and perimeter routing to guarantee the delivery of the geocast packet to all nodes in the region. The algorithm solves the region gap problem in sparse networks, but it causes unnecessary overhead in dense networks. The proposed geodirection-cast is a geographic routing instead of an ad hoc routing protocol. Geographic routing has several advantages: nodes require information only from their direct neighbours so discovery floods and state propagation are not required, and accordingly it has lower overhead and faster response to dynamics. Geographic routing is more scalable than ad hoc routing protocols and more suitable for WSN. Therefore, the proposed mechanism has an advantage than the forwarding mechanisms that are mentioned earlier. Most of the forwarding mechanisms use single forwarding path to send the data packets toward the destination area in geographic region. However, the delivered

3192

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

packets are not guaranteed to arrive at their destination region. In contrast, the geodirection-cast uses quadrant multi-hop forwarding toward the destination, which provides more guaranteed delivery ratio. 2.2. Real-time routing protocol A comprehensive review of the challenges and the state-of-theart of real-time communication in sensor networks can be found in [10]. In this paper, the most common WSN routing protocol is presented. Chenyang Lu et al. develop real-time architecture and protocols (RAP) based on velocity [11]. RAP provides service differentiation in the timeliness domain by velocity-monotonic classification of packets. Based on packet deadline and destination, its required velocity is calculated and its priority is determined in the velocity-monotonic order so that a high velocity packet can be delivered earlier than a low velocity one. Similarly, SPEED is a stateless protocol for real-time communication in WSN. It bounds the end-to-end communication delay by enforcing a uniform communication speed in every hop in the network through a novel combination of feedback control and non-deterministic QoS aware geographic-forwarding [4]. MM-SPEED is an extension to SPEED protocol [12]. It was designed to support multiple communication speeds and provides differentiated reliability. Scheduling messages with deadlines focuses on the problem of providing timeliness guarantees for multi-hop transmissions in a real-time robotic sensor application [13]. In such application, each message is associated with a deadline and may need to traverse multiple hops from the source to the destination. Message deadlines are derived from the validity of the accompanying sensor data and the start time of the consuming task at the destination. The authors propose heuristics for online scheduling of messages with deadline constraints as follow: schedules messages based on their per-hop timeliness constraints, carefully exploit spatial reuse of the wireless channel and explicitly avoid collisions to reduce deadline misses. A routing protocol called real-time power control (RTPC) uses velocity with the most energy efficient forwarding choice as the metrics for selecting forwarding node [14]. A key feature of RTPC is its ability to send the data while adapting to the power of transmission. However, RTPC, RAP, SPEED, MM-SPEED and [13] depend on the velocity which is not sufficient to provide high throughput in wireless communication. The best link quality usually provides low packet loss and energy efficient [15]. On the other hand, RTPC uses minimum hop count as a metric to provide energy efficient forwarding. However, the minimum hop count affects the delivery ratio [16]. A routing protocol based on link quality is proposed in [17]. In this research, the expected transmission count metric (ETX) is developed as a metric to select forwarding node. ETX finds paths

with the minimum expected number of transmissions (including retransmissions) required to deliver a packet all the way to its destination. This metric predicts the number of retransmissions required using per-link measurements of packet loss ratios in both directions of each wireless link. The primary goal of the ETX design is to find paths with high throughput, despite losses. However, ETX does not consider the remaining power and end-to-end deadline. An energy-aware multi-path routing scheme, called as maximum capacity path scheme (MCP scheme), is proposed in [18]. In the MCP scheme, the sensor network is constructed as a layered network at first. Based on the layered network, every sensor node selects a shortest path with maximum capacity to sink. In order to improve the performance of MCP scheme, a path switching function is added to MCP scheme, denoted as MCP with path switching (MCP-PS) scheme. In MCP-PS, a node can switch the routing path to its neighbours in order to share the traffic. In multi-path routing schemes [19], sensor nodes have multiple paths to forward their data. Each time data is sent back to sink, a sensor node picks up one of its feasible paths based on special constrains such as maximum available energy or minimum delay. However, the mechanisms in [18,19] are not meant for real-time (end-to-end deadline) communication in WSN which is the main objective in this paper. 3. Design of RTLD in WSN In order to develop real-time routing in WSN, the packet velocity is utilized in the forwarding calculation. The wireless link quality at the physical layer is studied to predict the communication between sensors. In addition, the remaining power is estimated to spread all traffic load distribution during path forwarding to the destination. In Fig. 2, RTLD consists of four functional modules that include location management, routing management, power management and neighbourhood management. The location management in each sensor node calculates its location based on the distance to three pre-determined neighbour nodes. The power management determines the state of transceiver and the transmission power of the sensor node. The neighbourhood management discovers a subset of forwarding candidate nodes and maintains a neighbour table of the forwarding candidate nodes. The routing management computes the optimal forwarding choice, makes forwarding decision and implements routing problem handler. 3.1. Routing management The routing management consists of three sub functional processes; forwarding metrics calculation, forwarding mechanism and routing problem handler. The optimal forwarding calculation

Fig. 2. Block diagram of RTLD routing protocol.

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

is used to calculate next hop based on the forwarding metrics that include PRR, packet velocity and remaining power. The routing problem handler is used to solve the routing hole problem due to hidden sensor nodes in WSN. Unicast and geodirectional-cast are the mechanisms used to select the way to forward data. 3.1.1. Optimal forwarding determined In order to carry out the optimal forwarding calculation, the routing management calculates three parameters namely packet velocity, link quality and remaining power (remaining battery) for every one-hop neighbours. Eventually, the router management will forward a data packet to the one-hop neighbour that has an optimal forwarding. The optimal forwarding (OF) is computed as follows:

OF ¼ maxðk1  PRR þ k2  V batt =V mbatt þ k3  V=V m Þ where k1 þ k2 þ k3 ¼ 1

ð1Þ

where V mbatt is the maximum battery voltage for sensor nodes and is equal to 3.6 V [20]. V m is the maximum velocity of the RF signal that is equal to the speed of light. The determination of PRR, V batt and V is elaborated in the following section. The values of k1; k2 and k3 are estimated by exhaustive search using network simulator-2 (NS-2) simulation such that k1 þ k2 þ k3 ¼ 1 as illustrated in [21]. In [21], the number of possible values for each k is 11 (from 0.0 to 1.0) and the number of trials for event k1 þ k2 þ k3 ¼ 1 is 66. The optimal trail from 66 trials has been determined using NS-2 simulation with four types of grid network topology which are low density, medium density, high density with one source and high density with several sources. In each type of topology, three types of traffic load are examined. The finding in [21] shows that the trial with 0.6, 0.2 and 0.2 for k1; k2 and k3 experiences high performance in term of delivery ratio and power consumption. Therefore, Eq. 1 can be written as

OF ¼ 0:6  PRR þ 0:2  V batt =V mbatt þ 0:2  V=V m

ð2Þ

The average delay to one-hop neighbour (N) from the source (S) can be calculated as follows:

Avg delayðS; NÞ ¼ T c þ T t þ T p þ T q þ T b þ T s ¼

Round trip time 2 ð3Þ

where, T c is the time it takes for S to obtain the wireless channel with carrier sense delay and backoff delay. T t is the time to transmit the packet that is determined by channel bandwidth, packet length and the adopted coding scheme. T p is the propagation delay that can be determined by the signal propagation speed and the distance between S and N. In sensor networks, the distances between sensor nodes are normally very small, and the propagation delay can normally be ignored. T q is the processing delay which depends on network data processing algorithms to process the packet before forwarding it to the next hop. T b is the queuing delay, which depends on the traffic load. In a heavy traffic case, queuing delay becomes a dominant factor. T s is sleep delay which is caused by nodes periodic sleeping. When S gets a packet to transmit, it must wait until N wakes up. Eq. 3 shows that the delay between two pair of nodes varies since the T c and T b delays differ for all nodes. It is interesting to note that the routing management is independent of synchronization timing. The non-synchronization is solved by inserting the transmission time in the header of request to route (RTR) packet. When receiving node N replies to sensor node S, it inserts the RTR transmission time in its reply. Once S receives the reply, it subtracts the transmission time from the arrival time to calculate the round trip time. The packet velocity (V) between a pair of nodes is calculated as follows:

V¼

dðS; NÞ Avg delayðS; NÞ

3193

ð4Þ

where dðS; NÞ is the one-hop distance between source node S and destination node N. In this study, this distance is assumed to be fixed. However, if the sensor node is mobile, the distances can be calculated from the signal strength as shown in [22,23]. If the velocity is high, the packet has a high probability to arrive before deadline and thus ensure real-time communication. In designing RTLD routing protocol, the link quality is considered in order to improve the delivery ratio and energy efficiency [16]. It should be noted that the link quality is measured based on PRR to reflect the diverse link qualities within the transmission range. PRR is approximated as the probability of successfully receiving a packet between two neighbour nodes [16,24]. If PRR is high that means the link quality is high and vice versa. The PRR uses the link layer model derived in [16,25] as

"



8 PRR ¼ 1  15

   16   #176 1 X 1 j 16 ð1Þ exp 20cðdÞ 1 16 j¼2 j j



ð5Þ where cðdÞ is SNR and it can be calculated as

SNR ¼ cðdÞ ¼ Pt  PLðdÞ  Sr

ð6Þ

where Pt is the transmitted power in dBm (maximum is 0 dBm for MICAz), Sr is the receiver’s sensitivity in dBm (95 dBm in MICAz) [26]. PLðdÞ is the path loss model which can be calculated as

PLðdÞ ¼ PLðd0 Þ þ 10b log

  d þ Xr d0

ð7Þ

where d is the transmitter–receiver distance, d0 is the reference distance, b is the path loss exponent (rate at which signal decays) which depends on the specific propagation environment. For example, b equals to 2 in free space and will have larger value in the presence of obstructions. This work estimates the value of to be in b between 2.4 and 2.8 as calculated in [23]. X r is a zero-mean Gaussian distributed random variable in (dB) with standard deviation r. In order to compute the remaining power in the battery of a sensor node, MICAz has an accurate internal voltage reference that can be used to measure battery voltage ðV batt Þ. Since the eightchannel ADC on the microcontroller of MICAz (ATMega128L) uses the battery voltage as a full scale reference, the ADC full scale voltage value changes as the battery voltage changes. In order to track the battery voltage, the precision voltage reference (band gap reference) is monitored to determine the ADC full-scale (ADC_FS) voltage span which corresponds to V batt [26]. The battery voltage is computed as follows:

V batt ¼

V ref  ADC FS ADC Count

ð8Þ

ADC_FS equals 1024 while V ref equals 1.223 volts and ADC_Count is the ADC measurement data at internal voltage reference. 3.1.2. Forwarding mechanisms The routing management proposes two different types of forwarding in RTLD: unicast forwarding and geodirectional-cast forwarding towards the destination based on quadrant. In unicast forwarding, the source node checks the forward flag of each neighbour in the neighbour table. If the forward flag is 1, the source node will check the optimal forwarding metrics and compute forwarding progress as in Eq. (2). This procedure continues until the optimal forwarding choice is obtained. If there are no nodes in the direction to the destination, the source node will invoke the neighbour discovery. Once the optimal forwarding choice is obtained, the data packet will be unicast to the selected

3194

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

node. This procedure continues until the destination is one of the selected node’s neighbours. Directional forwarding is defined as forwarding to the next nodes that have the best progress towards the destination. In geodirectional-cast forwarding, if a node wants to forward a data packet to a specific destination in a specific geographical location, it will broadcast the packet in the first hop to all neighbours. Then the selected neighbouring node will use unicast forwarding to forward the packet towards the destination. Therefore, if the neighbouring nodes are in the same quadrant as the destination and if the distance to the destination is less than the distance from source to destination, nodes will forward the packet using unicast forwarding. Otherwise, the packet will be ignored. Since nodes have information of its neighbours, it will not only forward but also select a neighbour that has the optimal forwarding progress towards the destination. If the destination receives multiple copies of the same packet, it will accept the first packet delivered and ignore the others. The geodirection-cast mechanism is a modification of our previous work done on Q-DIR [27]. In Q-DIR, all forwarding nodes broadcast the packet without knowing the distance. However, in the proposed mechanism, source node only broadcasts the packet to one-hop neighbour. This modification of Q-DIR will save power usage, reduce packet flooding and minimize collision. Fig. 3 shows an example of geodirectional-cast forwarding of 12 nodes in a global coordinate system based on quadrant system. In this figure, S broadcasts the data packet to its neighbours. S considers D to be in the first quadrant. Nodes B, C, F, and N ignore the forwarding request because they are not in the same quadrant as D. Node L also ignores the forwarding request because its distance to D is greater than the distance between S and D. On the other hand, nodes A and G are in the first quadrant as D and the distance between them and D is less than the distance between S and D. Hence A and G will participate and forward the data packet to E and M, respectively. It is interesting to note that nodes A and G will use unicast forwarding to forward the data packet to E and M rather than broadcast. The forwarding policy may fail to find a forwarding node when there is no neighbour node currently in the direction of destination. The routing management recovers from these failures by using routing problem handler as described in the following section.

3.1.3. Routing problem handler A known problem with geographic-forwarding is the fact that it may fail to find a route in the presence of network holes even with neighbour discovery. Such holes may appear due to voids in node deployment or subsequent node failures over the lifetime of the network. Routing management in RTLD solves this problem by introducing routing problem handler which has two recovery methods; fast recovery using power adaptation and slow recovery using feedback control packet. The fast recovery is applied when the diameter of the hole is smaller than the transmission range at the maximum power. The routing problem handler will inform neighbour discovery to identify a maximum transmission power required to efficiently transmit the packet across the hole as shown in Fig. 4. In this figure, if nodes A and G are failures due to some problems such as diminishing energy of sensor node or due to unreliable connection, S will use maximum transmission power (0 dBm in IEEE 802.15.4) to send RTR. Therefore, node E will receive RTR from S and will reply using maximum transmission power. Hence, node E will be used as OF node. If the fast recovery can not avoid routing hole problem, the slow recovery is applied. In the slow recovery, candidate OF node will send feedback packet to its parent. The feedback packet will inform the sensor node parent to stop sending data packet toward OF sensor node. When the parent received feedback control packet, it will calculate OF again for all candidates as depicted in Fig. 5. In this case, node G has a hole routing problem. Therefore, node G sends feedback to node S that will select node A as OF. 3.2. Location management The proposed location management determines localized information of sensor nodes. It assumes that all sensor nodes are in a fixed position. It also assumes that the sink node is at the origin (0,0) and at least two of its neighbours are location aware. The location management is used to determine the sensor node location in a grid of WSNs. It assumed that each node has a location aware mechanism such as in [27,28] to obtain its location in the WSNs area. The location mechanism uses at least three signal strength measurements extracted from RTR packets broadcasted by pre-determined nodes at various intervals. Each pre-determined node broadcasts RTR packet and inserts its location in the

Fig. 3. Geodirectional-cast forwarding based on quadrant.

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

3195

Fig. 4. Fast recovery of routing hole problem.

Fig. 5. Feedback mechanism in routing problem handler.

packet header. The distance of the unknown node from the predetermined nodes is determined from the signal strength received based on a propagation path loss model of the environment. If the distance and location of these pre-determined nodes are known, unknown nodes can triangulate their coordinates as explained in [22,23]. The developed location management does not require additional hardware such as GPS since it uses the existing wireless communication hardware. 3.3. Neighbourhood management The design goal of the neighbourhood manager is to discover a subset of forwarding candidate nodes and to maintain neighbour table of the forwarding candidate nodes. Due to limited memory and large number of neighbours, the neighbour table is limited to a small set of forwarding candidates that are most useful in meeting the one-hop end-to-end delay with the optimal PRR and remaining power. The neighbour table format contains node ID, remaining power, one-hop end-to-end delay, PRR, forward flag, location information and expiry time as shown in Fig. 6. The proposed system manages up to a maximum store of 16 sensor nodes information in the neighbour table.

3.3.1. Neighbour discovery The neighbour discovery procedure is executed in the initialization stage to identify a node that satisfies the forwarding condition. The neighbour discovery mechanism introduces small communication overhead. This is necessary to minimize the time it takes to discover a satisfactory neighbour. The source node invokes the neighbour discovery by broadcasting RTR packet. Some neighbouring nodes will receive the RTR and send a reply. Upon receiving the replies, the neighbourhood management records the new neighbour in its neighbour table. Initially, the neighbour discovery will broadcast the RTR at the default power level. However, if the source node does not receive a reply from any node, the routing problem handler will be invoked. 3.4. Power management The main function of power management is to adjust the power of the transceiver and select the level of transmission power of the sensor node. It significantly reduces the energy consumed in each sensor node between the source and the destination in order to increase node lifetime span. To minimize the energy consumed, power management minimizes the energy wasted by idle listening

3196

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

Fig. 6. Neighbour table format.

Fig. 7. Power management in RTLD.

and control packet overhead. The transceiver component in MICAz consumes the most energy compared to other relevant components of the MICAz. The radio has four different states: down or sleep state (1 lA) with voltage regulator off, idle state (20 lA) with voltage regulator on, send state (17 mA) at 1 mW power transmission and receive state (19.7 mA) [20]. According to the data sheet values, the receive mode has a higher power consumption than the all other states. Fig. 7 shows the flowchart diagram of power management. In this figure, the sensor node sleeps most of the time and it changes its state to idle if it has neighbour in the direction of destination. In addition, if the sensor node wants to broadcast RTR, it changes its state to transmit mode. After that, it changes to receive mode if it waits replies or data packet from its neighbour. Since the time taken to switch from sleep state to idle state takes close to 1 ms [28], it is recommended that a sensor node should stay in the idle state if it has neighbours with forward flags equal to 1. Thus, the total delay from the source to the destination will be decreased. The power management also proposes that a sensor node should change its state from idle to sleep if it does not have at least one neighbour in the neighbour table that can forward data packet towards the destination. In addition, figure shows that if the battery voltage is less that 2.4 V, the sensor node will not be able to send or receive packets [20].

Table 1 Simulation parameters Parameter

IEEE 802.15.4

Propagation model Path loss exponent Shadowing deviation (dB) Reference distance (m) Packet size phyType macType freq_ Initial energy Power transmission Traffic

Shadowing 2.5 4.0 1.0 70 byte Phy/WirelessPhy/802_15_4 Mac/802_15_4 2.4e+9 3.3 J 1 mW CBR

4. Simulation implementation of RTLD NS-2 simulator has been used to simulate RTLD routing protocol. IEEE 802.15.4 MAC and physical layers are used to reflect real access mechanism in WSN. To create a realistic simulation environment, the RTLD has been simulated based on the characteristics of the MICAz mote from Crossbow [26]. Table 1 shows the simulation parameters used to simulate RTLD in NS-2. Many-to-one traffic pattern is used which is common in WSN applications. This traffic is typical between multiple source nodes and a base station. In this work, 121 nodes are distributed in a 100 m  100 m region

3197

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

RTLD is compared with three other baseline protocols that consider multiple packet speeds (MM-SPEED), velocity with energy efficiency (RTPC) and link quality (LQ) in routing decisions. The link quality forwarding policy selects next hop based on the highest PRR in the neighbour table. MM-SPEED selects the next hop based on the proper packet speed options that meets the end-to-end deadline. The feedback control and differentiated reliability in MM-SPEED routing protocol have not been taken into account in this work because they require modification on the MAC layer protocol. RTPC protocol forwards the packets to the most energy efficient forwarding choice that meets the packet’s velocity. The simulation evaluates the performance of all the forwarding policies in a situation where by the neighbour table of each node does not have forwarding choices. The routing protocols simulated in this work are location based routing protocols. In all the simulations, each node updates its neighbour table every 180 s. RTLD has been simulated with two different type of forwarding methods: RTLD with geodirectional-cast forwarding (RTLDG) and RTLD with unicast forwarding (RTLDU). 4.1. Effect of end-to-end packet deadline

Fig. 8. Network simulation model.

as shown in Fig. 8. Nodes numbered as 120, 110, 100 and 90 are the source nodes and node 0 is the base station node (sink). To increase the hop count between sources and the sink, the source nodes from the leftmost grid of the topology and the sink in the middle of the grid were selected. Also, the traffic has been assumed to be constant bit rate (CBR). End-to-End packet delay, packet delivery ratio, normalized control packet overhead and energy consumption are the metrics used to analyze the performance of RTLD. All metrics are defined with respect to the network layer. Packet delivery ratio is the ratio of packets received at the destination to the total number of packets sent from the source in a network layer. Normalized control packet overhead counts the number of control packets sent in the network for each data packet delivered while energy consumption is the total energy consumed in each sensor node during the simulation task.

a

RTLD U RTPC LQ MM-Speed

85

1 0.9 0.8 0.7

80

Delivery Ratio

Average EtE delay (ms)

b

95 90

The real-time transfer requires that each packet reaches its destination within the deadline period. The deadline delimits the lifetime of a packet traversing the WSN. In this simulation, Fig. 9(a) shows the average end-to-end delay comparison between RTLD and baseline routing protocols for different packet rate. RTLD possesses short average delay compared to baseline routing protocols. This is primarily due to its forwarding strategy which considers link quality and packet velocity that minimize the average delay. In the simulation, the end-to-end packet deadline was varied while the simulation time and traffic load were fixed at 100 s and 10 packet/s, respectively. The simulation results in Fig. 9(b) show that RTLD experiences higher delivery ratio by 4–7% than the baseline routing protocols. The finding also shows that RTLD provides the highest delivery ratio for all packet deadlines. This is primarily due to its forwarding strategy which chooses the next hop that has the optimal combination of the best link quality, remaining power and packet velocity. Besides that, Fig. 9(b) shows the minimum packet deadline is around 150 ms. Beyond this, the packet delivery ratio remains unchanged at its maximum throughput. The results in Fig. 9(a) and (b) justify that the end-to-end delay experience does not exceed the set limit 250 ms that also defined in [4,14]. It is important to note that the data packet travels between 5 and 10 hops to reach the sink in this simulation. However, the proposed system recommends more than 250 ms if the distance between the source and the sink is far away (more than 17 hops).

75 70 65

0.5 0.4 0.3

60 55 50

0.6

1

2

3

4

5

6

Packet rate (P/s)

7

8

9

10

0.2

RTLDU

0.1

RTPC LQ MM-Speed

0 50

75

100

125

150

175

200

225

Deadline (ms)

Fig. 9. Comparison between RTLDU and baseline routing protocols at: (a) different packet rate and (b) different packet deadline.

250

3198

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

a

b RTLD U RTPC LQ MM-Speed

0.7 0.65

Delivery Ratio

Packet Overhead per Packet Received

0.75

0.6 0.55 0.5 0.45 0.4 0.35 0

1

2

3

4

5

6

7

8

9

10

11

12

24

RTLD RTPC LQ MM-Speed

21 18 15 12 9 6 3 0

1

Packet Rate (P/s)

Energy per Packet (J)

c 0.09

2

3

4

5

6

7

8

9

10

Packet Rate (P/s) RTLD RTPC LQ MM-Speed

0.08

0.07

0.06

0.05

0.04 1

2

3

4

5

6

7

8

9

10

Packet Rate (P/s) Fig. 10. Comparison between RTLD and baseline routing protocols at different packet rate (a) delivery ratio; (b) normalized packet overhead; and (c) normalized energy consumption.

4.2. Impact of varying network load In this simulation, the packet rates were varied while the endto-end deadline and simulation time were fixed at 250 ms and 100 s, respectively. The traffic load is varied from 1 to 10 packet/ s to emulate low data rate in IEEE 802.15.4. The simulation results in Fig. 10(a) show that RTLDU experiences higher delivery ratio than the baseline protocols by 4% to 7% as observed in previous section. This is because RTLDU takes into consideration link quality, packet velocity and remaining power that guarantee high delivery ratio and energy efficient. This is primarily due to link quality with packet velocity are considered in RTLDU. The link quality consideration usually experiences high delivery ratio and energy efficient [15,16]. In addition, Fig. 10(a) shows the delivery ratio decreases as the load in the network increases. This is mainly due to packet loss because of network congestion and packet collision. Fig. 10(b) shows that RTLDU spends less number of packets overhead compared to baseline routing protocols. This is largely due to its neighbour discovery which does not allow the one-hop neighbour to reply if it is not in the direction to the destination. Hence, the probability of collision is reduced and packet overhead is minimized. On the other hand, the baseline forwarding strategy does not consider probability of collision due to neighbour discovery which degrades the delivery ratio and energy efficiency. Fig. 10(c) demonstrates that RTLDU consumes less power compared to baseline routing protocols because the packet overhead in RTLDU is less than the baseline routing protocols. 4.3. Prolonging WSN lifetime Network lifetime measures the amount of time before the first node runs out of battery power [23]. Based on this definition of WSN lifetime, this section will analyze the influence of the remaining power on the performance of WSN. End-to-end deadline, pack-

et rate and simulation time are fixed at 250 ms, 10 packet/s and 300 s, respectively. The simulation results in Fig. 11(a) show that the delivery ratio of RTLDU is higher by 5% up 15% compared to the baseline routing protocols. Table 2 shows WSN lifetime is prolonged by 16% using RTLDU compared to the baseline routing protocol. The baseline routing protocols suffer decreasing packet delivery ratio due to packet dropping over a long period of time. One major reason is the baseline routing ignores spreading of the traffic load among neighbouring nodes, hence creating routing holes problem. The routing holes problem may also appear due to power termination in the forwarding candidate node. In contrast, RTLDU distributes the load to forwarding candidates to overcome routing holes problem and hence, balancing the load among the neighbouring nodes and maintains the delivery ratio to a comparable level. Fig. 11(b) shows that RTLDU experiences the least packet overhead. Since baseline routing protocols are not capable of countering the routing hole problem, the nodes around the hole are required to send more packet overhead. RTLDU consumes up to 5% less power compared to baseline protocols as shown in Fig. 11(c). The reduced power consumption is as result of sending and distributing the load throughout the neighbouring nodes. 4.4. Enhancement of RTLD RTLDG routing uses geodirectional-cast forwarding mechanism. Simulation study on the influence of the forwarding mechanism is carried out using parameters configured in Table 1. The packet rates were varied while the packet lifetime and simulation time were fixed at 250 ms and 100 s, respectively. The traffic load is varied from 1 to 10 packet/s. The simulation results in Fig. 12(a) show that the RTLDG increases delivery ratio by 20% compared to RTLDU. This is due to feasible multiple paths forwarding in RTLDG. However, RTLDG drops sharply when the traffic load is high mainly due to congestion in the network. Moreover, the IEEE 802.15.4 MAC is designed for low traffic rate and does not work well with

3199

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

b Packet Overhead per Packet Received

a 0.55

Delivery Ratio

0.5 0.45 0.4 0.35 0.3

RTLDU RTPC LQ MM-Speed

0.25 0.2

8

6 5 4 3 2 1 0

180

200

220

240

260

280

RTLD RTPC LQ MM-Speed

7

300

200

210

220

230

Time (s)

c

250

260

270

280

290

300

Time (s)

0.12

RTLD RTPC LQ MM-Speed

0.11

Eneregy per Packet (J/P)

240

0.1 0.09 0.08 0.07 0.06 0.05 0.04 0.03

200

210

220

230

240

250

260

270

280

290

300

Time (s)

Fig. 11. Comparison prolonging lifetime between RTLDU and baseline routing at fixed packet rate (a) delivery ratio; (b) normalized packet overhead; (c) normalized energy consumption.

Table 2 Comparison WSN lifetime between RTLDU and baseline routing protocols Comparison Lifetime Normalized lifetime

RTLD 287.496 96%

RTPC 239.56 80%

MM-SPEED 230.395 77%

LQ 204.226 68%

high traffic load [29]. The flooding in the direction to the destination causes congestion near the source of the data packet, channel contention and interference. Fig. 12(b) shows RTLDG spends 4% higher packet overhead compared to RTLDU. Generally, this is due to data packet broadcast in the first one-hop when the packet travels from the source to the destination. Fig. 12(c) shows RTLDG consumes 9% more power compared to RTLDU to achieve high delivery ratio. This is largely due to its forwarding strategy spending more packets overhead for the initial broadcasting of packets. 4.4.1. Effect of routing problem handler In order to show the effect of routing holes problem, the WSN model has been simulated using random distribution topology in NS-2. In this scenario, 50 nodes are distributed in random topology as shown in Fig. 13. Node 10 is the source node and node 44 is the sink. In this case, nodes 20 and 26 have low battery power (0.9 J) and their energy diminishes shortly after packet forwarding. It is interesting to know that in NS-2, the sensor node will stop receiving and transmitting when the energy reaches zero. However, the MICAz sensor node stops receiving and transmitting once the remaining energy reaches 2.4 V. In Fig. 13, the data packet can flow through two paths according to the chosen OF strategy; the first path is from the source 10 to node 8, 2, 5, 0, 29, 45, 20, 47 and 44; and the second path is from the source 10 to node 8, 2, 5, 0,

29, 36, 26, 14 and 44. If the first path experiences routing hole problem along the path, feedback packet is sent to the sender of the data packet to stop data forwarding. According to the feedback mechanism, node 45 will discover from its neighbour table that node 20 anticipates energy problem. In this case, node 45 sends feedback packet to the previous hop, which is node 29 since it does not have other neighbour to forward the packet. Once node 29 receives the feedback packet, it will decrease the forwarding flag by 20%. This mean after five feedback packets are received at node 29 from node 45, node 29 will stop sending data packet to node 45. The feedback mechanism allows the node five chances to identify its case whether it has problem or not. After that, node 29 will search in its neighbour table for another neighbour that has active forwarding flag 1. As in Fig. 13, node 29 selects node 36 as an OF node. In the simulation, the traffic load is varied from 1 to 10 packet/s while the end-to-end deadline and simulation time were fixed at 250 ms and 300 s, respectively. The result in Fig. 14(a) shows that routing with feedback increases the delivery ratio by 15% as the packet rate varies. This is mainly due to the routing management is having heavy flexibility to deal with the routing problem. Besides, the feedback allows route recovery hence achieving higher throughput. The throughput without feedback becomes worst as the traffic load increases. This is due to the fact that the source node does not know the path status after its one-hop neighbour. Thus, more packets are lost as more traffic is generated. Fig. 14(b) shows that routing with feedback packet spends less number of packets overhead compared to routing without feedback packet. This is primarily due to the previous hop node in the routing without feedback is sending more packets overhead to recover the bad forwarding path even if there is no neighbour reply. Routing with feedback permits the previous hop node to

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

a

0.9

RTLDU RTLDG

0.85

Delivery Rate

0.8 0.75 0.7 0.65 0.6 0.55 0.5 0.45

1

2

3

4

5

6

7

8

9

b

25

Normalized Packet Overhead

3200

20

RTLDU RTLDG

15

10

5

0

10

1

2

3

4

Packet Rate (P/s)

c

6

7

8

9

10

180

RTLDU

160

Energy consumption (Joule)

5

Packet Rate (P/s)

RTLDG

140 120 100 80 60 40 20 0

1

2

3

4

5

6

7

8

9

10

Packet Rate (P/s) Fig. 12. Performance of RTLDG and RTLDU at different packet rate: (a) delivery ratio; (b) normalized packet overhead; (c) energy consumption.

packet consumes less power compared to routing without feedback packet. This is mainly due to higher packet overhead in the routing without feedback. 5. Experimental results of RTLD routing The RTLD routing protocol has been realized in real test bed using 25 sensor nodes (10 TelosB and 15 MICAz). Fig. 15 shows the picture of sensor nodes configuration and code uploaded onto the sensor through serial programming board for MICAz and USB port for TelosB. RTLD is lightweight mechanism, which can applied for different type of radio sensor board with different platform. The code size of RTLD routing protocol in the MICAz is 25,912 byte of flash memory and 1896 byte of RAM while the code size of RTLD in the TelosB is 32,212 byte of flash memory and 1207 byte of RAM because TelosB microcontroller and compiler are different. 5.1. Test bed network

Fig. 13. Random distribution topology.

send packets overhead only for a short time to confirm that the forwarding path is unstable. If the forwarding path is not recovered, the sensor node will launch feedback packet to its previous hop node. Otherwise, the previous hop node refrains from sending packet to sensor node. Fig. 14(c) shows that routing with feedback

WSN test bed has been used to verify the RTLD. The test bed performance in term of packet delivery ratio and average packet delay from the source to the destination are analysed. The results are compared with the simulation output. Many-to-one traffic pattern is used in RTLD routing protocol in the case of unicast forwarding mechanism. One-to-many traffic pattern is used in the geodirection-cast forwarding mechanism. In this work, 25 nodes are distributed in a 40 m  40 m region as shown in Figs. 16 and 17. Node numbered as 24 is the source node and node 0 is the sink. The traffic is CBR and locations of all nodes are known.

3201

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

b

0.65

RTLD With Feedback RTLD Without Feedback

0.6

Delivery Ratio

0.55 0.5 0.45 0.4 0.35 0.3 0.25 1

2

3

4

5

6

7

8

9

Packet Overhead per Packet Received

a

10

50 48

RTLD With Feedback RTLD Without Feedback

44 40 36 32 28 24 20 16 12 8 4 0 1

2

3

4

Packet Rate (P/s)

5

6

7

8

9

10

Packet Rate (P/s)

c 0.045 Energy per Packet (J/P)

0.042

RTLD With Feedback RTLD Without Feedback

0.038 0.034 0.03 0.026 0.022 0.018 0.014 0.01 1

2

3

4

5

6

7

8

9

10

Packet Rate (P/s) Fig. 14. Performance of RTLD using feedback packet at different traffic load (a) delivery ratio; (b) normalized packet overhead; (c) energy consumption.

Fig. 15. Programming sensor node.

5.2. Results of RTLD routing protocol in test bed The network in the test bed has been configured similar to the network in the simulation. In real test bed, end-to-end deadline and the experiment time were fixed at 250 ms and 100 s, respectively. The traffic load is varied from 0.2 to 2 packet/s. The results in Fig. 18(a) show that RTLD routing protocol in the simulation environment experiences slightly higher delivery ratio (about 5%) compared to the real test bed implementation. This may be due to the propagation model in the simulation differs from the real test bed environment. In practice, many parameters in the propagation model affect the signal strength including fading, reflection, diffraction and interference. In addition, it has been recommended by TinyOS that the maximum packet rate should be set to 0.5 packet/s for multi-hop communication because higher packet rates can lead to congestion and overflow of the communication queue [30].

Fig. 16. Network simulation grid.

Fig. 18(b) shows that the end-to-end delay in the real test bed is higher compared to the simulation study. The delay is largely due to the processing delay caused by the slow microprocessor in MICAz and TelosB compared to personal computer processing in the simulation. The microprocessor in MICAz and TelosB runs at 8 MHz while the processor in simulation runs at 1700 MHz which is more the 200 times faster. In addition, the delay can be due to unreliable communication links in wireless networks. The link failures cause retransmissions of the packet at the MAC layer which increase the average delay. Nevertheless, the end-to-end delay in

3202

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

Fig. 17. Network test bed field.

a

b

1

RTLD Simulation RTLD test bed

0.95

Average ETE Delay (ms)

0.85

Delivery Ratio

RTLD Simulation RTLD test bed 250

Threshold

0.9

300

0.8 0.75 0.7 0.65

Threshold 200

150

100

0.6

50 0.55 0.5 0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 0.2

2

0.4

0.6

0.8

Packet rate (P/s)

1

1.2

1.4

1.6

1.8

2

Packet Rate (P/s)

Fig. 18. Performance of RTLD test bed and simulation at different packet rate: (a) delivery ratio and (b) average ETE delay.

a

b

1

300

RTLD G Simulation RTLD G test bed

0.95

Average ETE Delay (ms)

250

0.9

Delivery Ratio

0.85 0.8

Threshold 0.75 0.7 0.65 0.6

150

100

50

RTLDG Simulation RTLDG test bed

0.55 0.5 0.2

Threshold

200

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Packet Rate (P/s)

Packet Rate (P/s)

Fig. 19. Performance of geodirection-cast in the test bed and simulation at different packet rate (a) delivery ratio and (b) average ETE delay.

the test bed is below the end-to-end deadline limit which is 250 ms.

Fig. 19(b) shows that the average delay in the test is higher than the simulation. This is mainly due to the similar reasons in Section 5.2.

5.3. Geodirection-cast forwarding in test bed 6. Conclusion The results in Fig. 19(a) shows the geodirection-cast forwarding mechanism in both simulation and test bed enhance the throughput by 10% higher than unicast forwarding mechanism due to the multi-path forwarding. This is extremely important in the realtime communication. However, this enhancement is achieved at expense of more power consumption. In addition, the results show the simulation environment experiences higher delivery ratio by 7.5% compared to test bed as the packet rate is varied. This is primarily due to the propagation model is affected by unpredictable parameters such as fading, reflection, diffraction and interference.

This paper presents the RTLDU and RTLDG design for WSN. RTLD is proposed to enhance the previous works by [4,11–14] in order to achieve high delivery ratio, minimum control packet overhead and efficient power consumption. In general, the finding concludes that RTLDU provides high delivery ratio and spends acceptable number of control packet overhead with comparable power consumption compared to RTPC, MM-speed, and LQ. RTLDG improves throughput compared to RTLDU at low traffic loads as it allows forwarding of data packets through multiple paths. However, RTLDG consumes

A.A. Ahmed, N. Fisal / Computer Communications 31 (2008) 3190–3203

more power due to the original sources are broadcasting data packets in the first one-hop neighbour to allow more than one possible multi-path. The significant feature of RTLD is that it distributes the task of load forwarding to all forwarding candidates in order to avoid packet dropping due to power termination and prolong the network lifetime. RTLD has been evaluated and verified in real experimental test bed network based on radio model of MICAz motes. References [1] F. Akylidiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communication Magazine 5 (2002) 2. [2] M. Chen, V.C.M. Leung, S. Mao, Y. Yuan, Directional geographical routing for real-time video communications in wireless sensor networks, Elsevier Computer Communications Journal 30 (17) (2007) 3368–3383. [3] K. Romer, F. Mattern, The design space of wireless sensor networks, IEEE Wireless Communications Journal 11 (6) (2004) 54–61. [4] T. He, J. Stankovic, C. Lu, T. Abdelzaher, SPEED: a stateless protocol for realtime communication in sensor networks, in: Proceedings of the 23rd International Conference on Distributed Computing Systems, 2003, pp. 46–55. [5] B. Karp, H. Kung, GPSR: greedy perimeter stateless routing for wireless networks, ACM MOBICOM (2000). [6] Y. Ko, N. Vaidya, Flooding-based geocasting protocols for mobile ad hoc networks, ACM/Baltzer Mobile Networks and Applications (MONET) Journal (2002). [7] Y. Ko, Any casting-based protocol for geocast service in mobile ad hoc networks, Computer Networks Journal (2003). [8] V. Park, M. Corson, A highly adaptive distributed routing algorithm for mobile wireless networks, IEEE INFOCOM (1997). [9] K. Seada, A. Helmy, Efficient and robust geocasting protocols for sensor networks, Elsevier Computer Communications Journal, Special Issue on Dependable Wireless Sensor Networks, 2005. [10] J. Stankovic, T. Abdelzaher, C. Lu, L. Sha, J. Hou, Real time communication and coordination in embedded sensor networks, Proceedings of the IEEE 91 (2003) 1002–1022. [11] C. Lu, BM. Blum, T. Abdelzaher, J. Stankovic, T. He, RAP: a real-time communication architecture for large-scale wireless sensor networks, in: Real-Time and Embedded Technology and Applications Symposium, IEEE conference, 2002, pp. 55–66. [12] E. Felemban, C. Lee, E. Ekici, R. Boder, S. Vural, Probabilistic qos guarantee in reliability and timeliness domains in wireless sensor networks, in: Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies, 2005, pp. 2646–2657. [13] H. Li, P. Shenoy, K. Ramamritham, Scheduling messages with deadlines in multi-hop real-time sensor networks, in: IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2005), San Francisco, California, March, 2005.

3203

[14] O. Chipara, Z. He, G. Xing, Q. Chen, X. Wang, C. Lu, J. Stankovic, T. Abdelzaher, Real-time power control in wireless sensor networks, Washington University in St. Louis, Tech. Rep. WUCSE-2005-31, 2005. [15] J. Zhao, R. Govindan, Understanding packet delivery performance in dense wireless sensor networks, in: Proceedings of the First International Conference on Embedded Networked Sensor Systems, USA, 2003. [16] M. Zuniga, B. Krishnamachari, Analyzing the transitional region in low power wireless links, sensor and ad hoc communications and networks, in: IEEE SECON 2004, First Annual IEEE Communications Society Conference, 2004, pp. 517–526. [17] D. De Couto, D. Aguayo, J. Bicket, R. Morris, High-throughput path metric for multi-hop wireless routing, in: Proceedings of the Ninth Annual International Conference on Mobile Computing and Networking (MOBICOM), USA, September 14–19, 2003. [18] S. Huang, R. Jan, Energy-aware, load balanced routing schemes for sensor networks, in: Proceedings of 10th International Conference on Parallel and Distributed Systems (ICPADS 2004), vol. 7–9, 2004, pp. 419–425. [19] X. Hong, M. Gerla, W. Hanbiao, L. Clare, Load balanced, energy-aware communications for Mars sensor networks, in: Proceedings of the aerospace conference, vol. 3, 2002, pp. 1109–1115. [20] Chipcon, CC2420 low power radio transceiver, http://www.chipcon.com. [21] A. Ali, R.A. Rashid, S.H.F. Arriffian, N. Fisal, Optimal Forwarding Probability for Real-time routing in Wireless Sensor Network, ICT-MICC 2007, Malaysia, May 14–17, 2007. [22] L. Latiff, A. Ali, C. Ooi, N. Fisal, Development of an indoor gps-free self positioning system for mobile ad hoc network (MANET), in: Proceedings of the 13th IEEE International Conference on networks, MICC-ICON 2005, Malaysia, November 16–18, 2005. [23] A. Ali, L. Latiff, N. Fisal, GPS-free indoor location tracking in mobile ad hoc network (MANET) using RSSI, in: RF and Microwave Conference, IEEE Proceedings, 2004, pp. 251–255. [24] K. Seada, M. Zuniga, A. Helmy, B. Krishnamachari, Energy-efficient forwarding strategies for geographic routing in lossy wireless sensor networks, in: IEEE SECON 2004, Santa Clara, CA, October 2004. [25] IEEE 802.15.4 Standard (2003) Part 15.4: wireless medium access control (MAC) and physical layer (PHY) specifications for low-rate wireless personal area networks (LR-WPANs), IEEE Standard for Information Technology, IEEESA Standards Board, 2003. [26] Crossbow technology, MPR/MIB User’s Manual, Rev. B, Document 7430-002106, April 2005. [27] L.A. Latiff, A. Ali, C.C. Ooi, N. Fisal, Implementation of a quadrant-based directional routing protocol (Q-DIR), in: Wireless Mobile Ad Hoc Network, NCS 2006, Chieng Mai, Thailand, March 28–30, 2006. [28] B. Bougard, F. Catthoor, D. Daly, A. Chandrakasan, W. Dehaene, Energy efficiency of the IEEE 802.15.4 standard in dense wireless microsensor networks: modeling and improvement perspectives. IEEE DATE 2005, March 2005, pp. 196–201. [29] J. Zheng, M.J. Lee, A Comprehensive. Performance Study of IEEE 802.15.4, IEEE Press, Wiley Interscience, 2006. Chapter 4, pp. 218–237. [30] UC Berkeley, TinyOS Tutorial, http://www.tinyos.net/tinyos-1.x/doc/multihop, last updated September 23, 2003.