A trustworthiness-enhanced reliable forwarding scheme in mobile Internet of Things

Journal of Network and Computer Applications 140 (2019) 40–53

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A trustworthiness-enhanced reliable forwarding scheme in mobile Internet of Things Jirui Li a , Xiaoyong Li a, ∗ , Xianglong Cheng a , Jie Yuan a , Rui Zhang b a Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China b School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA

A R T I C L E

I N F O

Keywords: Data forwarding Internet of Things Mobile Networks Reliable Trustworthiness

A B S T R A C T

In intelligent Internet of Things (IoT), finding a trustworthy route that has higher delivery ratio and lower transmission latency is a key challenge of data forwarding for mobile applications. This paper proposes a trustworthiness-enhanced reliable forwarding (TERF) scheme for mobile IoT to minimize the disadvantages of selfish or malicious nodes to data transmission. TERF is built on a dual trustworthiness framework that consists of the local and global trustworthiness among nodes. First, mobile IoT network is modeled as a weighted directed graph that changes over time. Second, based on the dynamic graph, TERF redefines the contact probability and the service degree, and employs them to measure the local trustworthiness between nodes. The former reflects one node’s familiarity with another node, and the latter helps reduce the interference from malicious nodes. They can improve the local trustworthiness prominently. Third, to refrain from one-sidedness of the local trustworthiness, TERF considers the role of nodes in the entire networks, and constructs the global trustworthiness between nodes based on the personal centrality and the social similarity. The social similarity reflects the associations between mobile device nodes, while the personal centrality reflects the relative importance of nodes and can avoid the inefficiency from selfish nodes. More importantly, TERF uses the dot product of the local trustworthiness vectors between two nodes to compute the social similarity, and employs the nodes’ degree and the local trustworthiness to calculate the personal centrality. Finally, the TERF algorithm is developed. The experimental results prove that TERF has high stability, and outperforms SimBet, PROPHET and Bubble Rap with respect to message delivery ratio, average latency, average hop-count distribution and the network cost.

1. Introduction Based on new mobile communication technologies (e.g., 5G) or flying wireless communication platforms (e.g., unmanned aerial vehicles) and so on, the traditional IoT applications can be expanded to form many intelligent and complex mobile IoT scenarios such as smart city, intelligent transportation, smart environmental monitoring and smart homes (Gubbi et al., 2013; Mozaffari et al., 2016; Shi et al., 2011). In such IoT scenarios, people can share information or contents very well, because mobile devices that are seen as wireless relays can provide more communication chances via utilizing low-cost short-range communication techniques (e.g., Zigbee, WiFi and Bluetooth) (Yang et al., 2013). However, the real network environments become more complicated as the number of mobile-connected devices

continues to grow, and the existence of a large number of invalid paths caused by malicious or selfish devices have reduced greatly the performance of IoT data forwarding services. Therefore, to improve data delivery ratio and provide guarantee for achieving the continuity of services in mobile IoT applications (Li et al., 2018a), it is crucial to improve the trustworthiness and effectiveness of data transmission between IoT devices by considering actual IoT environmental factors. 1.1. Motivations The participation of mobile devices carried by people has prompted researchers to consider the influence of human behaviour factors when designing data forwarding schemes. In recent years, many effective

∗ Corresponding author. E-mail addresses: [email protected] (J. Li), [email protected] (X. Li), [email protected] (X. Cheng), [email protected] (J. Yuan), [email protected] (R. Zhang). https://doi.org/10.1016/j.jnca.2019.05.003 Received 16 June 2018; Received in revised form 15 November 2018; Accepted 6 May 2019 Available online 13 May 2019 1084-8045/© 2019 Elsevier Ltd. All rights reserved.

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Journal of Network and Computer Applications 140 (2019) 40–53

forwarding protocols based on human behaviour have been proposed for different mobile IoT applications such as social networks, vehicular networks and underwater sensor networks. (Wang et al., 2010; Leguay et al., 2007; Erramilli et al., 2008) used physical contexts (e.g., the duration or number of contacts between nodes) to estimate the relay nodes for social applications in mobile IoT. However, they ignored the influence of social information/contexts (e.g., the contact probability between nodes, the centrality of nodes or the similarity between nodes) on the network performance, which leads to the lack of reliability in the experimental results. (Zhu et al., 2013) proposed that the network performance is extremely dependent on social contexts acquired by mobility feature technologies, and two nodes contact more frequently if they have higher similarity (Li et al., 2015). For example, the social similarity between classmates is higher than that between students in different classes, which means that an information can be transmitted more quickly and safely among the classmates. In addition, the personal centrality of a node is more prominent if it is more important in the network (Yuan et al., 2015). For example, a node with higher personal centrality, such as the organizer of a corporation, has bigger chance to interact with other nodes. These studies such as (Li et al., 2014, 2015; Yuan et al., 2015; Daly and Haahr, 2007, 2009; Chang et al., 2015; Li and Das, 2013; Gao et al., 2013; Zhou et al., 2016; Jin et al., 2013; Tao et al., 2018; Jedari et al., 2017; Wu et al., 2018; Ali et al., 2018; Zhang and Cao, 2017; Zheng and Wu, 2017; Kafaie et al., 2018), had employed the social context factors to design some effective forwarding algorithms for sharing data or information in specific IoT scenarios. However, the above contexts-based forwarding schemes all did not pay attention to the construction of trustworthy relationships between nodes. A universal and extensible trustworthiness forwarding model for mobile IoT applications is still lack, and the existing studies have the following limitations. First, some studies, like (Li et al., 2014, 2015; Daly and Haahr, 2007, 2009) and so on, believed that two devices have the same trustworthiness to each other if they used the same social factors to compute the trustworthiness values. For example (Li et al., 2014), defined the contact probability between two nodes as the ratio of the number of their meetings to the total number of meetings between all network nodes. However, the trustworthiness between two devices cannot be equal in the actual IoT applications (Li et al., 2018b). Therefore, if we employ the inappropriate definitions of factors to define other factors such as the social similarity or the personal centrality, or develop the forwarding schemes based on these factors, it will induce a larger deviation between the calculation results and the fact. Furthermore, to ensure the security of data transmission, the basic trustworthy interaction relationships between IoT devices should be constructed based on many evidence-based or social factors (e.g., the contact probability and the service degree) (Yuan et al., 2015; Gao et al., 2013). However, some studies only used the contact probability that reflects the familiarity between node pairs to assess the trustworthiness relationships (Li et al., 2014, 2015; Tao et al., 2018; Jedari et al., 2017). The nodes with higher contact probability may be malicious nodes, which can bring huge potential safety hazard and lead to a decline in forwarding performance when using the social similarity or personal centrality based on the contact probability to design forwarding schemes. Finally, although many studies, that used simultaneously a variety of social factors such as the contact probability, the social similarity and the personal centrality to measure relay nodes, improved data delivery ratio to a certain extent, they not only ignored the impact of malicious or selfish nodes and the hierarchical difference between these factors, but also did not construct an appropriate model to integrate these factors. An inappropriate or inapplicable scheme can result in the untrustworthiness of forwarding decisionmaking.

1.2. Main idea and contributions Although there are many efficient solutions for the same problem, there is always room for enhancement or improvement due to some existing constraints (Li et al., 2017). According to the needs of the trustworthy forwarding, the paper first redefines several social factors based on the characteristics of mobile IoT, and considers them as the forwarding metrics among IoT devices. Then, a Trustworthiness-Enhanced Reliable Forwarding (TERF) scheme is designed, which does everything possible to improve the efficiency of IoT forwarding by enhancing the trustworthiness between nodes to reduce the impact of malicious or selfish nodes in mobile IoT. To our best knowledge, this work is the first to study a trustworthiness-enhanced forwarding pattern by combining several trustworthy social factors and mobile IoT environments. The main innovations of TERF go beyond those of existing approaches in terms of the following four aspects. 1. TERF uses a dual trustworthiness measurement framework to improve the reliability and effectiveness of data forwarding in mobile IoT scenarios. The dual trustworthiness framework refers to the local and global trustworthiness evaluation between nodes. The former assesses unidirectional trustworthiness relationships between nodes, and the latter evaluates two-way trustworthiness relationships among nodes. As a result, the framework can measure the dynamic relationships between mobile devices in real-time and provide a reliable guarantee for mobile IoT forwarding applications effectively. 2. TERF leverages a directed weighted graph model to construct the unidirectional trustworthiness relationships between nodes, which is more consistent with the practical demands for mobile IoT than the undirected graph or the weighted undirected graph. In the TERF model, the values of unidirectional trustworthiness relationships between nodes (namely the values of weighted edges between two nodes), are obtained by using two evidence-based social factors of nodes that are the contact probability and the service degree. The contact probability represents the familiarity between node pairs, and the service degree measures the reliability that a node provides service to other nodes. They can avoid hacker attacks or virus infections effectively and provide the most basic security guarantee for data forwarding in mobile IoT from the beginning. This model has a certain reference value for the construction of forwarding schemes in mobile IoT. 3. TERF uses the local trustworthiness and the nodes’ degree to build the global trustworthiness that includes the personal centrality of nodes and the social similarity between nodes. The global trustworthiness can avoid the one-sidedness of the local trustworthiness and is the supplement and improvement of the latter. Moreover, both the personal centrality and the social similarity are redefined in mobile IoT, which makes TERF scheme more suitable for the mobile IoT forwarding demands. 4. TERF scheme is evaluated by experimenting on two typical datasets that are the Reality Mining project and the North Carolina State Fair, and comparing with three well-known data forwarding protocols that include SimBet (Daly and Haahr, 2007), PROPHET (Lindgren et al., 2003) and Bubble Rap (Hui et al., 2011). Extensive simulation results and theoretical analysis confirm the security and effectiveness of the TERF scheme. For example, when testing on the Reality dataset, TERF with average value 51.1% is higher than SimBet with 12.2%, PROPHET with 14.6% and Bubble Rap with 37.97% in cumulative delivery ratio. Meanwhile, in terms of average delivery latency, average hop-count, and cost ratio, the performance of TERF is about 2 times higher than that of the other three algorithms. The remainder of the paper is organized as follows. We overview the related work in Section 2. After giving the mobile IoT model represented by a directed weighted graph, Section 3 analyzes some trustworthy social factors based on nodes’ past social interactions. In Section 41

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4, we first propose the TERF structure; secondly, we describe the calculation of the dual trustworthiness measurement framework including the local and global trustworthiness evaluation for reliable forwarding; finally, we design the TERF forwarding utility and algorithm, and analyzes the time and space complexity of the TERF. Section 5 presents the simulation parameters and performance assessment results of TERF. In Section 6, we draw our conclusions.

the future temporal centrality and closeness of nodes. The studies used the community structures or nodes’ movement features to ensure that the data is forwarded successfully during a connection between mobile nodes. Although the above studies provided certain security for data forwarding in mobile IoT, they ignored the impact of malicious or selfish nodes. To avoid the destruction from malicious or selfish nodes, researchers have proposed some data forwarding schemes with security enhancement, such as (Li et al., 2015; Yuan et al., 2015; Daly and Haahr, 2007, 2009; Chang et al., 2015; Li and Das, 2013; Jedari et al., 2017; Wu et al., 2018; Ali et al., 2018). The studies presented different methods and delivery probability metrics including encounter probability, location similarity, geometric distance, expected delay, social similarity, or time elapsed since last encounter. For each metric, different studies have different calculation indicators and methods. For example, Daly (Daly and Haahr, 2007) designed a SimBet algorithm based on the social similarity. The SimBet used the quantity of common neighbors between nodes and the probability of a node becoming a relay to measure social similarity and centrality of ego networks to different nodes, and it could enhance the probability of finding better relays to the final target node by choosing the nodes with relatively big SimBet values. Based on (Daly and Haahr, 2007), (Daly and Haahr, 2009) first presented seven social network metrics that come from the analysis of a node’s past social activities. Second, it selected the encounter probability, the closeness and the recency as the basic indicators to calculate a node’s tie strength relationship with the destination node, then designed a new data forwarding scheme. In view of the features of mobile social networks, Li (Li et al., 2015) only took advantage of the number of contacts between nodes to assess the contact probability, by combining which and community structure to measure local activity and social similarity. According to GPS traces of human walks from the real world, Yuan (Yuan et al., 2015) presented a newfangled data forwarding metric Hotent (HOTspot-ENTropy) to raise the performance of opportunistic forwarding, which employed the number of common neighbors between nodes to compute the similarity and did not consider the disadvantages of malicious nodes. Due to the existing of malicious nodes, the encounter probability does not truly present the node’s competency of delivering data in hostile wireless environment (Li and Das, 2013), proposed a trust-based forwarding framework to more accurately evaluate an encounter’s delivery competency. Apparently, the networks will show better performance via using social similarity and personal centrality simultaneously to build the forwarding utility function, because they can reflect a certain reliable trustworthiness between nodes and provide effective safety guarantee. To detect malicious nodes (Wu et al., 2018), proposed a security-oriented opportunistic data forwarding mechanism to detect the on–off attack for mobile social networks, which not only prevents malicious nodes from intercepting data packets, but also exploits the node collaboration to forward data packets. (Jedari et al., 2017) proposed a game theory incentive algorithm for social-aware routing to motivate social selfish nodes in data relay. One common critical phenomenon can be found in above-mentioned literatures: the bigger the personal centrality of nodes, the higher the probability that nodes can be chosen to relay; and if two nodes have higher social similarity, they will contact with a higher probability. Nevertheless, according to the calculation method of metrics, the above studies used a weighted or directed network model. Different from these works, we choose a directed weighted graph to construct the mobile network model, and exploit the contact probability and the service degree to measure the local trustworthiness that is unidirectional trustworthiness relationship between nodes. Then, combining them with the degree of nodes to evaluate social similarity and personal centrality that are two-way trustworthiness factors between nodes. Finally, the law of universal gravitation is used to evaluate the forwarding selection.

2. Related work In social networks, social trustworthiness refers to the phenomenon of mutual recognition, trust and sincere communication among certain members of society (Grabner-Kräuter and Bitter, 2015). For the current mobile IoT, there are many actual evidences that the trustworthiness between two nodes can be assessed based on their interactions or relationships, such as (Nitti et al., 2012, 2014; Chen et al., 2016; Wu et al., 2015; Ruef, 2002; Yan et al., 2014). Therefore, the idea of social trustworthiness can be used to solve the problem of the reliable forwarding in mobile IoT. There exist many classical data forwarding algorithms in social networks, such as Spray-and-Wait (Wang et al., 2010), PROPHET and Epidemic (Castellano and Pastor-Satorras, 2010), that are only the algorithms based on encounter. On this basis, nodes’ spatial or contextual information and the contact history among nodes are used to predict future contact probability. For instance, leveraging different contexts, most of studies based on Epidemic such as (Leguay et al., 2007; Tian et al., 2016; Ren et al., 2015), made a better tradeoff between the consumption of system resources and packet delivery ratio. For opportunistic networks, PROPHET was a probabilistic routing protocol and predicted the future contact probability between nodes by taking advantage of their encounter histories. In (Lindgren et al., 2003), the encounter history was maintained by each node, and on the basis of the encounter probability, the routing decision was made. In view of the social relationships between nodes (Guan et al., 2017), leveraged the residence times of message copies in the node to design an adaptive multiple spray-and-wait routing scheme. In addition, based on the node contacts in the trace (Tao et al., 2018), first investigated the activeness of nodes and the probability of reaching the destination, and then proposed a contacts-aware forwarding scheme to improve the transmission performance and reduce the extra network overhead for opportunistic forwarding decisions in mobile social networks. Apparently, to find an optimal relay in routes of above algorithm, the decision criteria was decided by the context factors or the contact history between nodes. However, in mobile IoT, the link loss between some objects is prone to occur when the holders or objects move around. To transmit data successfully in mobile IoT, many nodes’ behaviors based enhancement forwarding models are proposed, such as (Gao et al., 2013; Zhou et al., 2016; Zhang and Cao, 2017; Zheng and Wu, 2017; Hui et al., 2011). By exploiting the social community structure, the transient characteristics of contact distribution and network connectivity in delay tolerant networks (Gao et al., 2013), chose appreciate forwarding metrics based on the above patterns and designed an effective data forwarding scheme with a short time constraint. Based on the pairwise contact processes in delay tolerant networks (Zhang and Cao, 2017), detected transient communities by using a new contactburst-based clustering method. From the perspective of data delivery ratio, the more active person in a community is more likely to be a high-qualified data carrier. Bubble Rap (Hui et al., 2011) estimated the delivery ratio by exploiting the role of people in local social networks. (Zheng and Wu, 2017) found that mobile opportunistic social networks exhibit a nested core-periphery hierarchy, in which, few active nodes with large weighted degrees are the network core, while the network peripheries are composed of inactive nodes with small weighted degrees. Based on the average separating time and the variance of the separating time (Zhou et al., 2016), developed an efficient forwarding algorithm for online social networks via predicting 42

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3. Network model

the number of common friends, the closer the relationship between two members. That is, members having a stronger association with a given member are good relay candidates for message diffusion to that member. For example, students in the same class often know all the students in the class and establish good trustworthy relationship with each other, the messages can be transmitted safely to a target student. Therefore, in mobile IoT, the social similarity between nodes has an important influence on their trustworthiness relationship from a global perspective. However, the traditional definition of the similarity does not consider the problem of the selfishness of nodes even if two nodes may be hostile or friendly in actual IoT applications. This paper redefines the similarity between nodes to try to avoid the impact of selfish nodes and guarantee the reliable transmission of messages. Meanwhile, it is one-sided to assess the trust forwarding of packets by only using the similarity. (Freeman, 1979) showed that the centrality is related to “group efficiency in problem-solving, perception of leadership and the personal satisfaction of participants”. If a mobile device is involved in many transactions or has many relationships, its personal centrality can provide a peculiar information of the network and is expected to assume a central role in mobile IoT. Apparently, the centrality of nodes can also reflect the forwarding ability and the trustworthiness of nodes from a global perspective. Therefore, this paper chooses the social similarity and the personal centrality as two-way trustworthiness factors between node pairs to estimate relay nodes.

Here, we first introduce the network model of mobile IoT; Then, based on this model, we analyze the trustworthiness factors between node pairs in mobile IoT. 3.1. Dynamic directed weighted graph We express the mobile IoT model by using a dynamic directed weighted graph that changes with a time series. Definition 1. Dynamic Directed Weighted Graph G, = {G0 , G1 , … , Gt , · · · ∣ Gt = (Vt , Et , Wt )} where Gt = (Vt , Et , Wt ) represents the network snapshot at time t. Vt = {v1 , v2 , · · · vp , · · ·} is the set of nodes, Et = {< vp , vq > ∣ vp , vq ∈ Vt } is the set of edges, and Wt = {wtv v ∣ vp , vq ∈ Vt and < p q

vp , vq >∈ Et } is the set of weights on directed edges (e.g., vp → vq ) at time t. Both Vt and Et change over time. In particular, the value of wvp vq in this paper is based on the trustworthiness value on edge < vp , vq > at time t, which well reflects the asymmetry of the trustworthiness between two nodes and is more in line with the actual social IoT scenarios. The detailed computation of wtv v will be provided in p q Section 4.2. 3.2. Trustworthiness factors between mobile IoT devices

4. Trustworthiness-enhanced reliable forwarding (TERF) scheme

Although trustworthiness and trust may be different in contexts, in which the trustor needs to consider risk (Cho et al., 2011), trustworthiness and trust are treated the same for simplicity in this paper. To estimate the trustworthiness between nodes in mobile IoT, we identify two kinds of trustworthiness factors as follows. Unidirectional Trustworthiness Relationship. In social psychology, the trustworthiness is established through communication between people (Churchill and Mishra, 2017). (Daly and Haahr, 2009) analyzed several social factors, such as the probability, the intimacy and multiple social backgrounds, and proposed the relation strength between nodes can be measured based on one or more of these factors. (Granovetter, 1977) proposed that “the deeper the sentiments of friendship between people, the higher the probability the interaction between them”. Namely, the more the number of contacts between two nodes, the higher the encounter probability, and the more familiar to each other. Meanwhile, the trustworthiness between merchants will increase with the number of successful transactions. That is to say, in mobile IoT, the more the number of forwarding packets successfully, the higher the reliability of the nodes, and the higher the trustworthiness between nodes. Apparently, the number of contacts, the number of receiving packets and the number of forwarding packets are the main factors determining the strength and trustworthiness of a relation. In addition, due to the specific characteristics of mobile IoT, the trustworthiness between two nodes in mobile IoT is asymmetrical, which means that node N does not necessarily trust node M even if node M trusts node N (Cho et al., 2011). Therefore, employing the number of contacts and the historical behavior to construct the contact probability and service degree between nodes (they are the two important social attributes of nodes), can guarantee the most basic trustworthiness from one node to another node and avoid the interference from malicious or selfish nodes during data forwarding. Two-way Trustworthiness Factors. As unidirectional trustworthiness factors, both the contact probability and the service degree can only measure the directional trustworthiness between node pairs from the local view of points. However, for complex mobile IoT applications with large-scale structure, it is critical to assess the global ability of nodes when executing the packets forwarding business. Usually, the number of common friends of different members can explain the relationship between members in a certain sense. The more

Based on the trustworthiness factors between nodes in mobile IoT, we construct the TERF scheme that is built on a dual trustworthiness measurement framework consisting of the local and global trustworthiness. This section first shows the overall structure of TERF scheme, and then introduces the components and calculations of the dual trustworthiness measurement framework in detail. Finally, it proposes the TERF algorithm and analyzes its time complexity. 4.1. TERF structure The structure of TERF in mobile IoT is illustrated in Fig. 1. As seen in Fig. 1, the TERF scheme is based on a dual trustworthiness measurement framework. The relationships between mobile nodes can be obtained from the interactions between mobile devices in various mobile IoT scenarios such as the satellite network, social network and sensor network. The dual trustworthiness measurement framework is built on the relationships between nodes and made up of two parts that are the local trustworthiness evaluation and the global trustworthiness evaluation between node pairs. In the local trustworthiness evaluation, a node, based on its own interests, uses the unidirectional trustworthy relationships among nodes (namely, the contact probability and the service degree) to estimate the trustworthiness of its next-hop neighbors. Apparently, this evaluation and update module of the local trustworthiness can obtain firsthand evidence from the real interaction information between mobile devices in IoT. Note, The trustworthiness estimation is unidirectional, which obeys the rules that mobile IoT model is a weighted directed graph and can provide the most basic security assurances from the start. Nevertheless, if we only consider the local trustworthiness between nodes, there would be prejudice or unsafe in the results of forwarding judgment because of the existence of malicious or selfish nodes. For example, malicious nodes deliberately use disguised packages to interact frequently with some key normal nodes in advance, which makes malicious nodes obtain high contact probability and high service degree, and then achieve higher local trustworthiness. As a result, important data in applications can easily be intercepted, which will lead to data forwarding interrupt or privacy leakage and cause certain economic losses. Therefore, to obtain fair and effi43

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Fig. 1. The structure of TERF in mobile IoT.

cient relay nodes, based on the local trustworthiness, we construct the global trustworthiness relationship between nodes for the dual trustworthiness measurement framework. In global trustworthiness evaluation, the social similarity between nodes and the personal centrality of nodes are chosen to assess the global forwarding ability of next-hop nodes. The former is one of the most common indicators in many studies, reflecting the associations between node pairs in mobile IoT; the latter is another common indicator and reflects the relative importance of nodes, efficiently reducing the bad effect from selfish or malicious nodes in mobile IoT applications. Apparently, the mobile IoT is a complex hybrid network and changes over time. In our TERF scheme, IoT scenarios at different times determine its dynamics because of the continuous mobility of some IoT devices. Therefore, at each timestamp, the TERF scheme is executed according to the needs of forwarding data, and finally forms a series of dynamic changes in a continuous moment.

the range of Δt; h < vp , vq >tt −Δt represents the number that node vp contacts vq in the range of Δt. Clearly 0 ≤ Hvp ,vq ≤ 1. Service Degree. It can measure well the forwarding ability and reliability of nodes following the analysis principles of mobile IoT, and helps us identify malicious or selfish nodes effectively. We compute the service degree Zvp ,vq node vp providing to vq at time t based on Equation (2).

4.2. Dual trustworthiness measurement

expressed in Equation (3).

4.2.1. Local trustworthiness evaluation This paper employs the contact probability and the service degree as indicators to evaluate the local trustworthiness relationships between node pairs. Based on the demands of current mobile IoT scenarios, we redefine the two factors as follows. Contact Probability. The contact probability is related to the number of contacts between nodes at time t. If the sliding window length is set to Δt (0 ≤ Δt < t and Δt is a constant), then the contact probability Hvp ,vq on the edge < vp , vq > at time t is defined in Equation (1).

wtv

Zvp ,vq =

zf < vp , vq >tt −Δt zr < vp , u >tt −Δt

(2)

where zf < vp , vq >tt −Δt represents the number that vp forwards packets for vq from time t − Δt to t, and zr < vp , u >tt −Δt represents the total amount that vq recieves packets from all other nodes from time t − Δt to t. Apparently 0 ≤ Zvp ,vq ≤ 1. The above two factors are computed following the evidence-based strategies. Then, at time t, the weight vector wtv v of the edge vp → vq is p q

Hvp ,vq =

h < vp , vq >tt −Δt ht < vp , u >)tt −Δt

p vq

= (Hvp ,vq , Zvp ,vq )

(3)

Apparently, all the elements wtv

p vq

in Wt construct a weighted adjacent

matrix at time t that records each edge weight of the network snapshot, which are used to measure the global trustworthiness factors (the social similarity and the personal centrality). 4.2.2. Global trustworthiness evaluation Social Similarity. The social similarity among nodes represents their associations in mobile IoT. Its value can be measured by using Euclidean distance (Drusvyatskiy et al., 2017), Manhattan distance (Clempner and Poznyak, 2017),cosine angular distance (Andoni et al., 2015) and Hamming feature distance (Zhai et al., 2018) and so on. Nevertheless, for the actual mobile IoT, the above-mentioned

(1)

where u denotes any one node in Vt at time t, and ht < vp , u >tt −Δt represents the overall number that node vp contacts all other nodes in 44

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methods like distance-based cannot provide a significant explanation. For example, suppose that u is a node with forwarding data, and v is the target node; l and k are two nodes encountered by u simultaneously at a certain time; the weight vectors between l, k, u and v are wvl = wvk = wvu = (0.5, 0.4), wlv = (0.4, 0.5), wkv = (0.6, 0.7) and wuv = (0.2, 0.1), respectively. If we use Euclidean distance (suppose D) to compute the similarity between l, k, u and v, √ then the distance√values are D(v, l) = (0.5 − 0.4)2 + (0.4 − 0.5)2 ≈ 0.141, D(v, k) = (0.5 − 0.6)2 + (0.4 − 0.7)2 ≈ 0.316 and D(v, u) = √ (0.5 − 0.2)2 + (0.4 − 0.1)2 ≈ 0.424, respectively. If we think that the smaller the distance, the higher the similarity, then l is chosen as the next relay node; conversely, u continues to carry forward data at the current time. However, in fact, compared with l and u, the probability that k contacts with v successfully is greater because of 0.6 > 0.4 and 0.6 > 0.2, and the service degree that k provides to v is also higher due to 0.7 > 0.5 and 0.7 > 0.1, obviously, k is more suitable for transmitting data than l and u. It is because that, in our network model, the weight vector on each edge represents the link strength among two nodes. Usually, the higher the values of all weight factors between two nodes are, the greater their link strength is. Meanwhile, the similarity between two nodes is proportional to their link strength, namely, the more frequent the two nodes contact and the more times they provide services to each other, the higher their similarity is. In addition, the dot product method is a binary operation that receives two vectors on real number set and returns a real scalar, which can reflect the importance of each element of vectors at the same time. Therefore, the paper uses the dot product method to assess two nodes’ social similarity value because it can focus on the distribution of each factor in link strength vector, and the definition of social similarity is as following.

at time t, the degree centrality of node vp is defined in Equations (4) and (5). ∑ Dtv ∣in = mtv′ v (4) p p v′ ∈Vt

Dtv ∣out = p

(5)

′

pv

p

Similarly, mtv

′

t, if not, mtv

= 0.

= 1 if node v′ is the next-hop vertex of node vp at time

pv

′

pv

According to the definition of the social similarity, we know that the probability factor is also directly proportional to the service degree factor. Thus, to simplify the follow-up calculation of node centrality, we temporarily consider the linear sum of all factors in a link vector as ′ the edge weight. Then, at time t, numerical form of the weight wtv v of p q

the edge vp → vq is shown in Equation (6). ′

wtv

p vq

= 𝛼 Hvp ,vq + 𝛾 Zvp ,vq

(6)

where 𝛼 + 𝛾 = 1(0 ≤ 𝛼, 𝛾 ≤ 1), 𝛼 and 𝛾 are the proportionality factors, which are used to measure the relative importance of the contact probability and the service degree, respectively. To facilitate the calculation of following examples, in this section, we believe for the time being that these two factors are important equally, namely 𝛼 = 𝛾 = 0.5. Besides, in the section 5.4, we will validate the influence of their different combination values on the performance of TERF scheme. In a weighted graph, degree can generally be extended to the sum of weights (Barrat et al., 2004; Newman, 2004; Opsahl and Panzarasa, 2009; Opsahl et al., 2010). Therefore, in the weighted directed graph, for a given node vp at time t, its degree centrality is computed in Equations (7) and (8). ∑ ′ WDtv ∣in = wtv′ v (7)

q p

p q

mtv

p

edges vp → vq and vq → vp at time t, respectively, the social similarity between node vp and vq at time t is defined as SStv ,v = wtv v • wtv v . p q

v′ ∈Vt

where, mtv′ v = 1 if edge v′ → vp exists at time t, otherwise, mtv′ v = 0.

Definition 2. (Social Similarity): Given the two link strength vectors wtv v = (Hvp ,vq , Zvp ,vq ), wtv v = (Hvq ,vp , Zvq ,vp ) that denote the weights of p q

∑

q p

Herein, the symbol • represents the dot product between two vectors.

p

According to the definition of social similarity based on the dot product method, in the above example, the similarity values between u, l, k and the target node v are as following.

WDtv ∣out =

SStv,l = wvl • wlv = (0.5, 0.4) • (0.4, 0.5) = 0.40;

where wtv′ v

p

∑ v′ ∈Vt

′

wtv′ v

SStv,k = wvk • wkv = (0.5, 0.4) • (0.6, 0.7) = 0.58;

p

p

′ ′

pv

wtv

′ ′

pv

Now, if we only consider social similarity as the forwarding condition, both l and k are suitable as the relaying nodes based on SStv,l > SStv,u and SStv,k > SStv,u . But because of SStv,k > SStv,l , at last, we will choose node k to transmit the message continually. Subsequently, node k will keep the similar computing steps like the above methods. Apparently, when we employ the dot product method to assess the similarity between two nodes, the different social characteristics of nodes are no longer detailed distinction. It simplifies the computing process of social similarity between nodes and makes it be more in line with the actual network environments. Personal Centrality. In network and graph theory analysis, node centrality represents its relative importance in the graph. Usually, the more important the individual, the higher the probability to relay a message. That is to say, the personal centrality reflects the active degree of the individual in an entity network. For the measurement of personal centrality, the most common ways include Freeman’s degree, closeness, betweenness measures (Freeman, 1977, 1979) and eigenvector centrality method (Bonacich and Lloyd, 2015). Degree represents the number of one-hop neighbors of the current given node. The higher the degree centrality of a node, the bigger the chance to connect to other nodes. In directed graph, a node’s degree includes two parts that are the in-degree and the out-degree. Therefore,

(8)

′

pv

is greater than 0 if edge v′ → vp exists at time t, or else, wtv′ v

Similarly, wtv

SStv,u = wvu • wuv = (0.5, 0.4) • (0.2, 0.1) = 0.14.

′

wtv

represents an element of the weighted adjacency matrix,

p

′

p

v′ ∈Vt

′

= 0.

is greater than 0 if edge vp → v′ exists at time t, or else,

= 0.

Apparently, when the weight of each edge is 1 (the network is binary), Equations (7) and (8) are equal to Equations (4) and (5), respectively. On the contrary, the outcomes of degree centrality with or without weights are different. As shown in Fig. 2, for node vq and vl , Dtv ∣in = Dtv ∣in = 2 and Dtv ∣out = Dtv ∣out = 2, but WDtv ∣in = 2.6 and q

q

l

q

l

WDtv ∣in = 1.7, WDtv ∣out = 2.4 and WDtv ∣out = 1.6. Obviously, here node q

l

l

vq should be easier to be chosen as a relaying node than vl in the actual networks. In fact, because Equations (7) and (8) only consider the overall level of a node participating in the network and ignore the number of its one-hop neighbors, they are blunt measures. For example, for node vq and vm in Fig. 2, WDtv ∣in = WDtv ∣in = 2.6 and q

m

WDtv ∣out = WDtv ∣out = 2.4, but the number of nodes connected to node q

m

vq and vm is two and three, respectively. Clearly, node vm has greater probability to relay a message successfully than node vq . Based on the above analysis, when computing the centrality of a node, we consider Equations (4), (5), (7) and (8) simultaneously. Referring to (Opsahl et al., 2010), we also use a parameter 𝛽 to adjust the relative importance of the degrees of a node compared to the edge weights associated with this node. Therefore, in this paper, a node’s personal centrality is defined as the product of its degree and the average weight to these edges associated with this node adjusted by 𝛽 . 45

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with the target node and the initial carrier has a very low social similarity with the target node, the social similarity-based forwarding scheme will increase the delivery delay and the forwarding cost. Therefore, to bypass their disadvantages and make best use of their advantages, it is very crucial to how to integrate the two social factors. In 1687, Isaac Newton presented the law of universal gravitation, and it means that any two particles attract each other by force in the direction of the centerline. The gravitational force between two particles is directly proportional to the product of their mass, and inversely proportional to the square of their distance. Furthermore, it does not focus on the chemical nature or physical state of two objects and the intermediary substance. (Yuan et al., 2015) and (Gao et al., 2013) used the law to compute the gravitation between two nodes for making data forwarding decision. (Andreev, 2015) applied the law of universal gravitation to predict the dynamics of a socio-economic system. Based on the law (Zhang and Chow, 2015), proposed a new gravity model for location recommendations to exploit the spatiotemporal sequential influence on location recommendations. Accordingly, in our paper, we specifically adopt the law to incorporate personal centrality and social similarity. Node’s personal centrality is regarded as the mass, and the social similarity between nodes is regarded as the distance. Then, the gravitation gvt ,v between node vp and vq is described in Equation (12).

Fig. 2. A mobile IoT senciro with 6 nodes and 12 weighted edges.

At time t, the in-degree centrality and out-degree centrality of node vp are shown as follows. ( PCvt ∣in = Dtv ∣in × p

p

WDtv ∣in Dtvp ∣in (

WDtv ∣out p

PCvt ∣out = Dtv ∣out × p

)𝛽

p

Dtvp ∣out

p

(

= Dtvp ∣in )𝛽

)1−𝛽 ( )𝛽 WDtv ∣in

(9)

p

(

= Dtvp ∣out

)1−𝛽 ( )𝛽 WDtv ∣out

(10)

p

p q

PCvt PCvt p q gvt ,v = G ( )2 p q SStvp ,vq

For mobile IoT represented by directed graphs, the number of edges that originate from a given node vp stands for its activity, and the amount of edges that toward vp reflects its popularity. Both of them are in direct proportion to personal centrality of a node. To simplify the calculation, we choose the linear sum of a node’s popularity and activity to assess this node’s personal centrality, which is shown in Equation (11).

(12)

where, G ≈ 6.67 × 10−11 is the universal gravitational constant. Clearly, the bigger the gravitation between a node and the target node, the higher its probability to relay packets. The forwarding process of TERF is outlined in Algorithm 1.

PCvt = PCvt ∣in + PCvt ∣out p

p

p

( )1−𝛽 ( )𝛽 ( )1−𝛽 ( )𝛽 WDtv ∣in + Dtv ∣out WDtv ∣out = Dtv ∣in p

p

p

Algorithm 1 Data forwarding based on TERF. Input: the node with transmitted content u; the target node d; Output: the relaying node r. 1 for any transmitted content C in node u queue do 2 calculate gut ,d ; 3 Tempt = gut ,d ; //Tempt is a temporary variable 4 for each node vp met by u do 5 if vp == d 6 node u transmit C to d; 7 remove C from vp ; 8 return 𝜙 and exit; 9 else 10 calculate gvt ,d ;

(11)

p

The effect of 𝛽 on nodes’ centrality values for the network in Fig. 2 is illustrated in Table 1. Apparently, the nodes’ centrality values are equal to the sum of their own Dtv ∣in and Dtv ∣out when 𝛽 = 0, and when 𝛽 = 1, the nodes’ centrality values are equal to the sum of their own WDtv ∣in and WDtv ∣out . When the sum of all factors in link strength vector is fixed and 0 < 𝛽 < 1, the higher the node’ degree is, the bigger its centrality value is. Conversely, when 𝛽 > 1 and the sum of all factors in link strength vector is fixed, nodes’ centrality value will decrease with the increasing of their degree. For example, although node vq and vm have the same value for all factors in link strength vector, PCvt is bigger than PCvt when q

p

m

𝛽 = 0.5, while PCvt q is smaller than PCvt m when 𝛽 = 1.5. Furthermore, when nodes have the same degree, regardless of 0 < 𝛽 < 1 or 𝛽 > 1,

11

if Tempt < gvt

12

Tempt = gvt

13 14 15 16 17 18 19 20 21 22 23 24

these nodes’ centrality value PCvt will increase with the increasing of the sum of WDtv ∣in and WDtv ∣out , such as node vp , vk and vn , or node vq and vl . Therefore, as a positive adjustment parameter, 𝛽 can be assigned according to the actual needs of the research. If setting 0 < 𝛽 < 1, a node with high degree is favorable, but a node with low degree is taken as favorable when setting 𝛽 > 1. 4.3. TERF forwarding utility and algorithm In the process of data forwarding, the personal centrality and the social similarity are two independent factors that are used to measure the forwarding ability of nodes. Usually, in mobile IoT applications, nodes’ personal centrality follows a power law distribution, which reflects that the number of nodes with high personal centrality is very few, meanwhile, these nodes are most likely to have no close connection with the target node. Thus, they will delay the forwarding. In addition, when there exist many intermediate nodes having high social similarity

then

p ,d p ,d

;

endif endif endfor if Tempt > gut ,d then r ← select the node corresponding to the Tempt ; node u transmits C to node r; return r; else node u maintains C; return 𝜙; endif endfor

Algorithm 1 shows how does node u transmit the data to the target node d. when it meets several nodes at time t. To transmit data to d successfully, u first needs to gain gut ,d and store it to a temporary variable Tempt . Then, u determines whether each node vp that encounters u is d. If vp is d, then u directly transmits data to d, otherwise, u computes every gvt ,d and stores the largest gvt ,d to Tempt . Finally, comparing Tempt p

46

p

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Journal of Network and Computer Applications 140 (2019) 40–53

Table 1 Node’s Degree Centrality Results When 𝛽 is Assigned Different Values. Node

vp

vq

vm

vk

vl

vn

Dtv ∣in

1

2

3

1

2

1

Dtv ∣out

1

2

3

1

2

1

WDtv ∣in

1.6

2.6

2.6

0.6

1.7

0.9

WDtv ∣out

1.2

2.4

2.4

1.1

1.6

1.3

PCvt ∣in when 𝛽 =

0 0.5 1 1.5

1 1.26 1.6 2.03

2 2.28 2.6 2.96

3 2.79 2.6 2.42

1 0.77 0.6 0.47

2 1.84 1.7 1.57

1 0.95 0.9 0.85

PCvt ∣out when 𝛽 =

0 0.5 1 1.5

1 1.1 1.2 1.31

2 2.19 2.4 2.63

3 2.68 2.4 2.15

1 1.05 1.1 1.15

2 1.79 1.6 1.43

1 1.14 1.3 1.48

PCvt when 𝛽 =

0 0.5 1 1.5

2 2.36 2.8 3.43

4 4.47 5 5.59

6 5.47 5 4.57

2 1.82 1.7 1.62

4 3.63 3.3 3

2 2.09 2.2 2.33

nodes that u encounters is 𝜑 in any forwarding decision, then, the time complexity is max(O(n(n − 1)), O(n𝜒𝜑)) and the space complexity is O(n2 ) for the TERF algorithm.

Table 2 The Gravitation between Nodes vm , vk , vq and vl . 𝛽

PCvt

PCvt

PCvt

PCvt

gvt

gvt

gvt ,v

0 0.5 1 1.5

6 5.5 5 4.6

2 1.8 1.7 1.6

4 4.5 5 5.6

4 3.6 3.3 3

120 100 85 74

104 106 109 111

1200 993 825 686

m

k

q

k ,vm

l

q ,vm

l m

Proof 1. In TERF scheme, the greater computation mainly includes the calculation of the weights and the running complexity of Algorithm 1. When computing the weights in the directed graph model, the time complexity is O(n(n − 1)) because the total number of nodes is n at time t. In Algorithm 1, due the average number of relay nodes that u encounters is 𝜑 in any forwarding decision, the time complexity of the most inner “for” cycle is O(𝜑). Meanwhile, at time t, the node u needs to forward 𝜒 contents, and the time complexity of the most outer “for” cycle is O(𝜒). Then, for n nodes, the average time complexity is O(n𝜒𝜑). Due the values of n, 𝜑 and 𝜒 are uncertain, the total time complexity of TERF scheme is max(O(n(n − 1)), O(n𝜒𝜑)). Seen from the TERF scheme, the operation occupying large storage space is also the computation of weights in the directed graph, and its space complexity is O(n2 ). For Algorithm 1, the average space complexity is O(𝜒), and usally 𝜒 ≪ n. Thus, the total space complexity of TERF scheme is O(n2 ).

and gut ,d . If the former is larger, then u chooses the node r corresponding to the Tempt and transmits data to r, and next, r will execute Algorithm 1 to look for d. Otherwise, u continues to maintain the transmitted data until the data is forwarded to other nodes or expired. Suppose, in Fig. 2, node vm is the destination node, and node vk will transmit a message to vm . At time t, vk encounters vl and vq . According to Definition 2, the social similarity between node vm , vk , vq and vl are shown as follows. SStv ,v = (0.4, 0.2) • (0.5, 0.6) = 0.32, k m

Based on the dual trustworthiness relationships between nodes in mobile IoT, TERF scheme is designed. It is self-adaptive and has several advantages. First, employing the directed weighted graph to construct the network model for mobile IoT is more suitable for the indeed mobile IoT scenarios. Second, compared with only considering the contact probability, using both the contact probability and the service degree to measure the local trustworthiness relationships among nodes can significantly improve the reliability of TERF. Third, choosing the social similarity and the personal centrality as the metrics to assess the global trustworthiness relationships between nodes can avoid the onesidedness of the local trustworthiness evaluation. All in all, TERF not only can pick more reliable relay nodes, providing better guarantee for data protection, but also has more powerful extensibility and flexibly changes the framework elements to meet the forwarding requirements of various mobile IoT scenarios.

SStv ,v = (0.2, 0.6) • (0.9, 0.5) = 0.48, q m SStv ,v = (0.4, 0.3) • (0.1, 0.3) = 0.13. l m

then, based on Equation (12) and Table 1, the gravitation between node vm , vk , vq and vl is calculated and shown in Table 2. Note that the final values of gvt ,v , gvt ,v and gvt ,v are the correk m

q m

l m

sponding values in Table 2 multipled by the constant G, respectively. Seen from Table 2, gvt ,v > gvt ,v and gvt ,v < gvt ,v when 𝛽 = 0, k m

q m

k m

l m

only node vl can be chosen as the next hop. When 𝛽 > 0, both gvt ,v q m and gvt ,v are larger than gvt ,v , but gvt ,v > gvt ,v , thus node vl is still l m

k m

l m

q m

the optimal relay node. In short, regardless of 𝛽 , gvt ,v is always the l m biggest one under these circumstances and node vl is always selected to relay a message. Afterwards, according to the above methods, node vl keeps on executing the same operations until a message is transmitted to the target node successfully.

5. Performance evaluations This section evaluates the performance of TERF scheme by experimenting on two typical datasets and comparing with three well-known data forwarding protocols.

4.4. TERF complexity analysis Theorem 1. At time t, suppose the total number of nodes is n, the number of contents that the node u transmits is χ, the average number of relay 47

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Journal of Network and Computer Applications 140 (2019) 40–53

pose that the total 3000 messages are generated and need to be forwarded, and TTL ranges from 0 s to 18,000 s. In addition, based on the characteristics of WiFi, the nodal transmission range is set to 250 m.

Table 3 Parameters and values. Parameter

Value

packet size the number of packets on each node data transmission speed buffer size of each node the update interval

50-100 KB 1000 2 Mbps 5 MB 5 min

5.2. Metrics

• Cumulative Delivery Ratio (CDR). The index indicates the ratio of the number of successfully transmitted packets to the total number of sent packets, and reflects the transmission performance of the overall network. • Average Delivery Latency (ADL). It represents the average time spent for all packets to be sent to the destination successfully. The smaller the indicator value, the smaller the index, the smaller the probability that the system resources will be occupied for a long time. • Average Hop-count Distribution (AHD). The indicator represents the distribution of hops for transmitting packets successfully. It reflects the social distance between origination-destination node pairs. • Average Network Cost (ANC). It quantifies the network overhead, which value depends on the total number of forwarded packets and the number of successfully delivered messages, and is equal to the proportion of the difference between the former and the latter out of the former. This indicator is directly proportion to the energy-saving effect of the network.

5.1. Experimental datasets and simulation settings At present, there is no publicly-available dataset to experiment with mobile IoT. Therefore, in this paper, we choose two real datasets for mobile social networks (a specific scenario of IoT) to validate the performance of our TERF. One is the Reality Miming project that describes a large and dense network scenario, the other is the North Carolina State Fair that is a small and well-connected scenario. 5.1.1. The reality dataset The Reality Mining project is a typical dataset in mobile social networks (Eagle and Lazer, 2013). It is a real-world trace and was conducted from September 2004 to June 2005 at the MIT Media Laboratory. In this paper, we extract the needed trace data that mainly includes MAC address and ordinal value of phones, communication phones. In order to make the experiment press close to the actual situation, we use all 94 subjects, and randomly choose the source node and target node at time t. The total number of the emulation is 20 times with different random source and target pairs, and the update interval between the two adjacent times is one. For the experiment, referring to (Li et al., 2015), all other parameters and values are listed in Table 3. In mobile IoT such as social networks, vehicular networks and road networks, the data-forwarding decision is mainly depended on the cumulative phenomena of various actual scenarios that need a period to reveal. Time To Live (TTL) is the longest survival time of a content in the network since it was created, and it can prevent some expired contents from further circulation. In the experiments, TTL is set from 1 h (h) to 2 weeks (w). In addition, because of the simple structure and limited hardware resources of some network devices, some factors such as long communication distance or node failure will lead to network link failure, which makes these devices become isolated nodes. In other words, isolated nodes refer to the device nodes that lose the communication with other devices in the network for a long time, which existence is a universal phenomenon in the actual mobile IoT scenarios. Thus, to better reflect the real performance of TERF, we simulate the experiments on the Reality Mining dataset and keep all isolated nodes generated during data extraction. The above settings are most consistent with the real mobile IoT scenarios.

5.3. Algorithm comparison In this section, to validate the robustness and effectiveness of the TERF scheme, we compare it against SimBet (Daly and Haahr, 2007), PROPHET (Lindgren et al., 2003) and Bubble Rap (Hui et al., 2011). The three algorithms are the most basic encounter-based strategy, meanwhile SimBet and Bubble Rap have social-ware properties further. 5.4. Results and analysis To embody well the adaptive of the TERF scheme, we select randomly five percent of the total number of nodes to act malicious or selfish nodes in all experiments. Meanwhile, we suppose “𝛼 = 𝛾 = 0.5” in advance when testing the influence of different 𝛽 on the TERF scheme. Then, we use the relative good 𝛽 to test the sensitiveness of different combinations of 𝛼 and 𝛾 to the TERF scheme. The experimental results on the Reality dataset are plotted in Fig. 3, which show the cumulative delivery ratio, average delivery latency, average hop-count distribution and average network cost for all delivered contents of TERF with four different 𝛽 values, SimBet, PROPHET and Bubble Rap algorithms, respectively. Fig. 3(a) shows the performance of cumulative delivery ratio. During the initial phase, all algorithms are almost the same. However, with the increasing of TTL, regardless of 𝛽 , TERF is always better than SimBet, PROPHET and Bubble Rap. Furthermore, seen from Fig. 3(a), SimBet performs the worst one, and reaches its peak value 25.1% at nearly time 1w, and then remains the same. For mobile networks, SimBet only considered the contact at some time points and did not consider the contact probability for some time. Therefore, if node vp transmits a content to another node vq having a contact with vp , but in future, vq does not contact with other nodes, the delivering will stop. Thus, in a weighted directed network, it is very important to consider the contact probability and the service degree between nodes. Meanwhile, after reaching the peak value 37.2% at time 1w, PROPHET appears the turning point because it needs superfluous relays to be adaptable to the changes of encounter probability, and then its delivery ratio declines; at about time 11 d, it keeps consistent with SimBet. Bubble Rap reaches the peak value 67% at about the 10th day. Clearly, in terms of the cumulative

5.1.2. The State Fair dataset The State Fair dataset was gathered by the literature (Rhee et al., 2011) and describes a well-connected scenario. In this dataset, 19 trajectories were gathered from 18 volunteers who visited a local state fair that includes many small street food stands, showcases and street arcades. The number of stay points ranges from 178 to 415 and its average is 288. More than one thousand people daily were attracted for 2 weeks as the event was very popular. The site is particularly small and each participant spent less than 3 h. Several studies, e.g., (Kaveevivitchai and Esaki, 2010; Rallapalli et al., 2010; Yuan et al., 2016), have applied it into different research fields to explore its characteristics such as intra/inter contact distribution. In this experiment on the State Fair Dataset, we choose the sourcedestination pairs randomly. Because this dataset is a small site, we sup48

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Journal of Network and Computer Applications 140 (2019) 40–53

Fig. 3. Simulation results on the Reality Mining dataset.

delivery ratio, its performance is better than SimBet and PROPHET, and is a little lower than any one of TERF with four different 𝛽 . Apparently, at close time 1 h, the delivery ratio of TERF with four different 𝛽 increase rapidly, and all of them reach the peak value at about time 4 d. Furthermore, when 𝛽 is assigned 0, 0.5 and 1 respectively, almost all the TERF’s peak values reach 74.3%, which is higher than that when 𝛽 = 1.5. While TTL varies from 1 h to 2w, with 𝛽 increasing, the cumulative delivery ratio of TERF basically shows a decreasing trend. The main cause is that we choose the contact probability and the service degree as the weight indicators in mobile networks. The values of two indicators are both in the range of [0,1] and are lower than the degree of nodes. According to Equation (11), the larger 𝛽 , the smaller the centrality value of node. Then, the smaller the gravitation between nodes maybe, the longer time it will take to extract a relay node. Finally, the message’s lifetime exceeds its TTL, and resulting in failure to deliver, and the cumulative delivery ratio declines. Although the delivery ratio of TERF with 𝛽 = 1.5 is the lowest in the four different values of 𝛽 , its peak value can also reach 69.7%, and its total performance far exceeds SimBet and PROPHET, and is somewhat better than Bubble Rap in cumulative delivery ratio. Of course, in terms of other three metrics, shown in Fig. 3(b), (c) and 3(d), when 𝛽 is assigned different values, the relation of four curves of TERF is also determined by the contact probability and the service degree among nodes. In Fig. 3(b), as a whole, the average delivery latency of SimBet, PROPHET and Bubble Rap are still higher than any one of TERF with four different 𝛽 , meanwhile, the results of Bubble Rap are close to

that of TERF and better than that of SimBet and PROPHET. Moreover, the latency performance of SimBet is a little better than that of PROPHET. This is mainly because, PROPHET algorithm needs to use redundant relays to complete successfully the message delivery as possible as, which can increase the delivery delay. The above-mentioned cause can be also used to explain the relationship between SimBet and PROPHET in Fig. 3(c) and (d). In addition, we find the latency of TERF with 𝛽 = 1.5 is a little lower than that of TERF with 𝛽 = 1, and this phenomenon can be also found in the tests of the average hotcount distribution and the network cost, shown in Fig. 3(c) and (d). This is mainly related to the Reality dataset used in our experiments. According to the table “friends” of the dataset, we build the network graph that is relatively sparse, in which, the degree of many nodes is smaller than their weight. Thus, based on Equation (12), TERF with 𝛽 = 1.5 can show better performance than TERF with 𝛽 = 1 in terms of the delivery latency, the average hop-count and the average network cost. Especially in terms of average network cost, illustrated in Fig. 3(d), regardless of 𝛽 , TERF always performs best among them. This is because, TERF can control the number of messages copies in sessions via preferring to choose the similar interests nodes as relays. Furthermore, with message TTL increasing, the network cost of TERF is descending. This is due to that TERF uses the dot product of forwarding utilities as social similarity strategy to relay messages. With the time going on, the social relationships between nodes are coming clearer and clearer, which makes TERF scheme be more suitable for mobile IoT and prompts the running efficiency of total network system because fewer message

49

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Table 4 The average value of four metrics in four algorithms. Average

TERF when 𝛽 =

Value

1.5

1

0.5

0

Rap

CDR(%) ADL(104 ) AHD ANC

47.5 5 3.96 55

49.2 5.22 4.11 63

53.4 4.2 2.61 54

54.3 3.8 2.65 49

38 5.9 4.9 2310

Bubble

SimBet

PROP HET

12.2 7.3 7.07 3010

14.6 7.5 7.6 5529

quently become mild. It is obvious that the CDR of TERF when 𝛽 = 0.5 is highest, and the three TERF when 0 ≤ 𝛽 ≤ 1 always outperform the TERF when 𝛽 = 1.5. In addition, regardless of 𝛽 , all TERF curves reach the maximum at about 1750 s and always perform better than SimBet, PROPHET and Bubble Rap. For example, the performance of the TERF when 𝛽 = 1.5, which is the worst one in the four different values of 𝛽 , still achieves up to a 16.4% improvement in CDR over SimBet, 13.7% over PROPHET and 4.1% over Bubble Rap. The main cause is that TERF employs double trustworthy mechanism including the local and the global to make forwarding decisions, the double metrics provide a fine-grained level for us to embody the relations between nodes. Therefore, it helps to choose the more reliable and higher quality relays for forwarding messages.

copies can be handled in the data forwarding. In addition, four TERF with different 𝛽 all keep a low network cost which are far smaller than the other three schemes. The main reason is that TERF scheme is good at finding high personal centrality nodes in pace with dynamic IoT. Based on the experiments with five percent malicious nodes, we list the average value of four metrics CDR, ADL, AHD and ANC in TERF, SimBet, PROPHET and Bubble Rap, shown in Table 4, which demonstrates again the superiority of our TERF algorithm. The experimental results on the North Carolina State Fair are shown in Fig. 4. Fig. 4(a) plots the performance of cumulative delivery ratio of all the mentioned algorithms with the changing of message TTL. Before about 750 s, the CDR of four TERF curves extremely increase, but subse-

Fig. 4. Simulation results on the State Fair dataset. 50

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Journal of Network and Computer Applications 140 (2019) 40–53

Fig. 5. The relative importance assessment of trustworthy factors on (a) Reality Mining and (b) State Fair, respectively.

Fig. 4(b) shows the performance in average delivery latency. Although the four TERF curves are very close, obviously, the ADL of the TERF when 𝛽 = 0 is in the optimization state and the TERF when 𝛽 = 1 has performed worst. Even so, regardless of 𝛽 , TERF always outperforms SimBet, PROPHET and Bubble Rap. For example, the TERF when 𝛽 = 1, with the highest ADL, still helps considerably in reducing up to 2.13 times, 2 times and 10% latency in SimBet, PROPHET and Bubble Rap. This reason is that we first employ two local metrics including the contact probability and the service degree to exclude the malicious nodes from the relay candidates. Because these malicious nodes only use the services from other members but don’t provide any service for other members, removing them helps to reduce the delivery latency. Fig. 4(c) clarifies that TERF has the better performance than the other compared algorithms in terms of the average hop-count distribution no matter which value 𝛽 takes. For example, TERF when 𝛽 = 1, one of the worst performing, can reduce the average hop-count by 50%, 47% and 12%, compared to the SimBet, PROPHET and Bubble Rap, respectively. The reason is the same with the average delivery latency. Fig. 4(d) illustrates the performance in the network cost. It is obvious that all TERF reduce the cost as well regardless of 𝛽 . For example, the TERF when 𝛽 = 1.5, which shows the worst network cost in terms of ANC, still gains better performance than Bubble Rap, because it reduces the average delivery latency and average hop-count by about 13.2% and 31%, respectively, while keeping a similar delivery ratio to Bubble Rap. In (Opsahl et al., 2010), the calculation method of node centrality (Equation (11)) had been validated that it is very suitable for graphs with large numerical weights. In this paper, based on the above experimental results, this conclusion is verified again. Additionally, it is apparent that all TERF show their absolute advantage than Bubble Rap, SimBet and PROPHET in terms of cumulative delivery ratio, mean latency, average hop-count and network cost. Known from the above experiments, the TERF scheme when 𝛽 = 0.5 performs better relatively than that when 𝛽 is set to other values. Subsequently, we use respectively the Reality dataset and the State Fair dataset to validate the sensitiveness of 𝛼 and 𝛾 for TERF with 𝛽 = 0.5 in the cumulative delivery ratio. The experimental results are plotted in Fig. 5. Fig. 5 plots the function curves of cumulative delivery ratio of TERF scheme with increasing TTL on the Reality Mining dataset and the State Fair dataset, respectively, when 𝛼 and 𝛾 are set to different values (𝛼 + 𝛾 = 1). Apparently, regardless of using the Reality Mining or the State Fair, the performance of TERF scheme when “𝛼 = 1, 𝛾 = 0”

is the best at the very beginning and then is worse than that when

𝛼 ≠ 0. The main cause is, “𝛾 = 0” means that the service degree of nodes does not work and the TERF only uses the contact probability to measure the basic trustworthiness relationships between two nodes. With the increase of TTL, the chance encountering selfish or malicious nodes becomes larger, and more and more forwarding packets are intercepted, which leads to the reduction of its CDR compared with that of TERF which uses the service degree and the contact probability simultaneously. Meanwhile, the CDR values of TERF when “𝛼 = 0, 𝛾 = 1” are always the lowest. It may be because, it ignores a large number of normal nodes that have high contact frequency with other nodes but provide fewer forwarding services temporarily for some reasons. However, regardless of in Fig. 5(a) or Fig. 5(b), the CDR of TERF when “𝛼 = 𝛾 = 0.5” is the first highest on average, although the curves are very close when 𝛼 ≠ 0 and 𝛾 ≠ 0, which again proves the rationality of the previous experimental results. Obviously, it is very necessary to assess the basic trustworthy relationships between nodes by employing the contact probability and the service degree at the same time. 5.5. Open issues and future work All experimental results have proved the accuracy and effectiveness of our TERF scheme, but we do not consider the dynamic partitioning problem of communities in mobile IoT and the energy consumption of transmitting messages. Meanwhile, the overall experiments are limited to specific IoT applications (e.g., mobile social networks) because of the high complexity of IoT environments and the lack of real datasets for mobile IoT. Thus, we suggest reflecting them in future research and experiment work. Besides, considering the security problem in the process of relaying messages, the trust factors between nodes should be focused on and would be another interesting future research topic. 6. Conclusion On the premise of ensuring the trustworthiness of forwarded data in mobile IoT, to improve quality of service and save more energy, some effective forwarding schemes are needed urgently. We design a powerful and practical trustworthiness-enhanced reliable forwarding scheme (TERF), which is built on the dual trustworthiness measurement framework that measures the global trustworthiness relationship (the social similarity and the personal centrality) among nodes via the

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local trustworthiness factors (the contact probability and the service degree) among nodes. The experimental results on both the Reality dataset and the State Fire dataset have proved that the TERF performs better for transmitting packets than SimBet, PROPHRT and Bubble Rap in the delivery ratio, the latency, average hop-count and average network cost.

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