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A Trust Cloud Model for Underwater Wireless Sensor Networks Jinfang Jiang, Guangjie Han, Chunsheng Zhu, Sammy Chan, and Joel J. P. C. Rodrigues

The authors study the problem of trust establishment between nodes in UWSNs. They first give a detailed overview of existing trust management mechanisms. Since UWSNs possess specific characteristics, it is noted that those existing mechanisms are not applicable for UWSNs. They then introduce a trust cloud model that is suitable for trust management in UWSNs.

Abstract Nowadays, the study of underwater WSNs (UWSNs) has become a hot topic. However, UWSNs have not been fully utilized in the complex underwater environment, since there are some difficulties in controlling mobile sensor nodes and underwater environment conditions. In addition, how to ensure the security of UWSNs and the safety of underwater mobile sensor nodes has not been solved well. In this article, we study the problem of trust establishment between nodes in UWSNs. We first give a detailed overview of existing trust management mechanisms. Since UWSNs possess specific characteristics, it is noted that those existing mechanisms are not applicable for UWSNs. We then introduce a trust cloud model that is suitable for trust management in UWSNs.

Introduction

Over the past few years, underwater wireless sensor networks (UWSNs) have gained much attention from researchers due to their wide applications in many underwater scenarios (oceanographic data collection, marine environment monitoring, ocean target surveillance, underwater assisted navigation, disaster forecast and prevention, etc.). As a kind of collaborative network, in almost all applications of UWSNs, sensor nodes are required to participate in the collaboration, while malicious attackers can seriously threaten the operation of UWSNs. Therefore, secure communication and collaboration among sensor nodes are needed to ensure the efficiency of UWSNs [1, 2]. Until now, there have mainly been three kinds of security mechanisms: intrusion protection, intrusion detection, and intrusion tolerance. As the first line of defense against malicious attackers, intrusion protection mechanisms use encryption algorithms, key management, and authentication technologies to prevent adversary invasion. They can resist external attacks well, but once the malicious attackers obtain secret keys and successfully initiate internal attacks, intrusion protection mechanisms lose their effectiveness. Intrusion detection mechanisms aim to detect and identify malicious attackers that have successfully invaded the network. However, intrusion detection mechanisms usually work after the initiation of malicious attacks. It is difficult to Digital Object Identifier: 10.1109/MCOM.2017.1600502CM

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detect malicious intruders as soon as possible; thus, real-time detection needs to be improved. Intrusion tolerance mechanisms try to protect networks while allowing the existence of malicious intruders. As the third line of defense, intrusion tolerance is considered to be an efficient security mechanism, and many new algorithms and technologies have been proposed to further improve network security. Trust management is an important part of intrusion tolerance mechanisms. As an effective complement to traditional cryptography, trust management is widely used in the Internet, terrestrial WSNs (TWSNs), point-to-point (P2P) networks, ad hoc networks, social networks, e-commerce, and so on. However, different application environments have different functional requirements for trust mechanisms. Traditional trust management mechanisms cannot be directly used in UWSNs. Due to the unique characteristics of the underwater environment and acoustic communication, the research on trust management mechanisms in UWSNs faces more challenges. In this article, we first give a detailed overview of existing trust management mechanisms, which are divided into seven categories according to the different theories or methods that are used to calculate trust. Each kind of trust management mechanism is presented in detail and compared carefully. Then this article proposes a novel trust evaluation algorithm, called the Trust Cloud Model (TCM), which can be used in mobile UWSNs. The new algorithm improves the trust calculation accuracy, and increases the successful communication rate of sensor nodes. In the remainder of this article, we first review existing trust management schemes. Then we describe how the cloud mathematical concept is used to evaluate the trust relationship for underwater sensor nodes. We give the details of our proposed TCM, which is a new trust model for UWSNs. We evaluate the proposed model using various performance metrics. Finally, we conclude the article.

Trust Management Schemes

As an important complement to traditional security defense based on cryptography, a trust management mechanism has a significant advantage in the identification of malicious nodes, and many trust management mechanisms have been proposed for WSNs [3]. The common practice of

Jinfang Jiang and Guangjie Han are with Hohai University; Chunsheng Zhu is with the University of British Columbia; Sammy Chan is with City University of Hong Kong; Joel J. P. C. Rodrigues is with National Institute of Telecommunications; Instituto de Telecomunicações, UBI; University of Fortaleza; and ITMO University. Guangjie Han is the corresponding author of thia article.

0163-6804/17/$25.00 © 2017 IEEE

IEEE Communications Magazine • March 2017

trust management is to first collect trust evidence, according to the behaviors of sensor nodes and then adopt some mathematical methods to deal with the trust evidence to further determine whether the node is credible or not. In this section, we divide existing trust management mechanisms into the following seven categories according to the different theories or methods that are used to calculate trust. Trust management based on subjective logic. In 2008, subjective logic was first adopted for trust computation [4]. The concepts of evidence space and concept space are introduced to describe and establish trust relationships. In addition, a set of logical operators are defined to calculate the trust values of sensor nodes. Subjective logic can also be adopted to study group trust relationship establishment. According to different group relations, direct trust and recommendation trust are measured. In 2014, Ren et al. [5] proposed a novel trust model based on subjective logic for intermittent connected networks. A set of trust similarity functions were defined to detect abnormal trust values so as to further improve trust evaluation accuracy. However, how to accurately obtain trust evidence has not been discussed in this kind of trust management mechanism. Trust management based on Bayesian theory. In 2004, Ganeriwal et al. proposed a trust mechanism based on Bayesian theory named Reputation-Based Framework for High Integrity Sensor Networks (RFSN) [6]. To the best of our knowledge, this was the first trust model proposed for WSNs, where sensor nodes monitor communication behaviors of their neighbor nodes. The amounts of successful and unsuccessful communication are counted for trust evidence, and are further used to obtain trust values of sensor nodes by using the Bayesian formula. At present, the trust model based on Bayesian theory is the most extensively used trust mechanism in WSNs. However, only considering successful or unsuccessful communication to evaluate trust is not reliable since communication behavior in UWSNs is easily affected by the environment. Trust management based on probability theory. In 2006, Crosby et al. proposed a trust calculation model based on probability theory [7]. First, through a simple statistical method, the relevant trust evaluation factors are obtained. Then the trust values of sensor nodes are calculated by the weighted algorithm. The proposed trust model is simple and has low computational complexity. However, the calculation is based on local monitoring, ignoring recommendations from other nodes in the network. In addition, it is hard to accurately obtain trust evaluation through the weighted algorithm, since it is difficult to determine the exact size of each weight value in UWSNs. Trust management based on fuzzy logic. In 2010, Chen et al. [8] studied the fuzzy nature of trust, and proposed a trust management mechanism based on fuzzy logic. Trust has subjectivity and fuzziness. In the trust management mechanism based on Bayesian theory and probability theory, randomness is used to express fuzziness, and the probability statistics method is adopted to calculate trust values of nodes. In the trust management mechanism based on fuzzy


logic, the fuzzy logic inference rule is established to express the subjectivity and fuzziness of trust, which can solve the problem of inaccurate trust calculation caused by the subjective fuzzy information. However, this kind of trust mechanism cannot provide a specific quantitative method for trust values. How to quantify the fuzzy trust relationship to specific trust values needs further study. Trust management based on D-S evidence theory. D-S evidence theory is an important method in uncertainty reasoning, since it can directly express “inaccuracy” and “uncertainty.” In 2011, Feng et al. [9] proposed a trust model based on node behaviors and D-S evidence theory. D-S evidence theory can describe the uncertainty of trust well. However, the computational complexity of D-S evidence theory is high, and grows exponentially with the increasing number of sensor nodes. Therefore, it is not suitable for resource constrained UWSNs. Another drawback of this kind of trust model is that it may not be possible to get the right result when conflict evidence is synthesized. Trust management based on entropy theory. Entropy is a measurement of information uncertainty. In 2006, Sun et al. [10] introduced entropy theory into trust evaluation for ad hoc networks. In 2008, Dai [11] et al. applied entropy theory for trust evaluation in TWSNs. In 2015, to solve the complex recommendation information processing problem, Zhang et al. [12] proposed a trust model based on entropy and recommendation chain classification. First, based on node honesty, recommendation chains are classified into different categories. Then direct trust and recommendation information are aggregated based on entropy theory. Compared to the traditional subjective model, trust management mechanisms based on entropy theory can get rid of trust fuzziness better and obtain accurate trust evaluation. Trust management mechanism based on cloud theory. In 1995, Li et al. proposed a cloud model based on the traditional fuzzy set theory and probability statistics theory [13]. In 2009, Ma [14] introduced a trust cloud into TWSNs, and put forward a cloud-based trust model (CBTM). CBTM does not take timeliness of trust into account. In addition, using the average method to evaluate trust values of sensor nodes is not reasonable. In 2014, Xu et al. designed a lightweight cloud model (LCT) for TWSNs [15]. The proposed trust model is simple: each sensor node can establish an independent LCT and carry out a comprehensive assessment of trust for its neighbor nodes. Cloud theory is mainly used for trust combination and transfer calculation. It can describe the uncertainty of trust well. However, how to obtain trust evidence and how to calculate trust values according to the trust evidence have not been resolved; moreover, in the process of trust combination and trust transfer calculation, conflict trust, trust repeated calculation, and the timeliness of trust are not taken into account. In addition, all the proposed trust models based on cloud theory are designed for the terrestrial environment, which cannot be directly used in UWSNs. Therefore, in this article, we introduce a trust model based on cloud theory for UWSNs.

As an important complement to the traditional security defense based on cryptography, a trust management mechanism has a significant advantage in the identification of malicious nodes, and many trust management mechanisms have been proposed for WSNs.

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The mathematical concept cloud can well describe the uncertainty

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sensor nodes, since they possess fuzziness and randomness.

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Figure 1. The sliding time window.

System Model Network Model

In the UWSN, there are n sensor nodes, denoted n by si  S, where S = {si} i=1. Each sensor node s i is randomly deployed at position pi. We assume that all sensor nodes have the same communication range. The communication radius is r. Only when the distance d(pi, pj) between two sensor nodes s i and sj satisfies d(p i,p j)  r can the two nodes directly communicate with each other. We call them one-hop neighbor nodes. The location of any sensor node can be obtained by possibly using GPS or other techniques such as triangulation or localization. In TCM, trust is periodically evaluated by using a sliding time window, as shown in Fig. 1. In each time slot, the information of node, flag, time, data, and trust is obtained, where node is the target node of current communication, flag records the communication status (successful or unsuccessful), time is the current

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Cloud Model

The mathematical concept cloud can describe the uncertainty of qualitative and quantitative values well. In our TCM, we use clouds to evaluate trust relationships for underwater sensor nodes, since they possess fuzziness and randomness. For any trust attribute X, if ∀x  X, where X is a trust evaluation domain denoted with accurate value, there is a mapping  satisfying  : X  [0, 1], x  (x)  [0, 1]; then the distribution of X in the domain X is called cloud. A model could consist of three digital features (E x, E n, E e) to describe transformation between qualitative and quantitative values. E x is the expected value of the attribute (i.e., the mean

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time, and data is the attribute data of the target node obtained in the process communication process. The attribute data is analyzed as trust evidence, which is further used to calculate trust. The calculated trust is recorded in trust.

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Figure 2. The architecture of TCM.

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Figure 3. The detail of TCM. value). E n is the entropy of the attribute, which reflects the ambiguity of Ex. Ee is the hyper entropy of the attribute, which indicates the uncertainty of En. Each cloud is composed of a lot of cloud drops. A single cloud drop cannot express any specific meaning or feature. But a cloud that consists of a number of drops can be a feature of a qualitative concept. For any xi, we can calculate (Exi, Eni, Eei) based on the Backward Cloud Generator as follows: Step 1. Calculating the mean x– i and the variance S2 of xi, 1 n 1 n xi = ∑ xi ,S 2 = ∑ (xi − xi )2 n i=1 n − 1 i=1 Step 2. Calculating Exi, Exi = x–i Step 3. Calculating Eni,

π 1 n × ∑ E xi − xi 2 n i=1 Step 4. Calculating Eei, Eni =

Eei = S 2 − Eni 2

Methods of Trust Evidence Collection

In this section, we introduce TCM in detail. Figure 2 presents a work flow of TCM. The detailed operation is presented as follows:

Trust Evidence Collection

The first step of trust management is the collection of trust evidence. As shown in Fig. 3, according to nodes’ communication behaviors, trust evidence is collected. There are two methods for trust evidence collection: neighbor nodes monitoring and direct information interaction. In UWSNs, abnormal acoustic communication (e.g., packet loss, packet error, and abnormal energy consumption) can be used as trust evidence to establish a trust model. A UWSN is characterized by narrow bandwidth, high bit error rate, and packet loss rate. In order to make full use of acoustic channel bandwidth, malicious packet error and packet loss must be avoided; thus, abnormal packet error and loss are used as trust evidence. In addition, as a kind of resource constrained network, a UWSN is especially limited by energy; thus, abnormal energy consumption is also taken into account.


or not. Thus, in the operation of the UWSN,

transmission. At the beginning of network deployment, there is no past communication between sensor nodes; thus, no trust evidence is available. In this case, we need to initialize the trust values of nodes. Here, we assume that all the sensor nodes are trustworthy at the beginning.

Direct Trust Cloud Establishment

Trust calculation is the core module in the trust management mechanism. In TCM, three kinds of trust are calculated: direct trust, recommendation trust, and indirect trust. For one-hop neighbor nodes, direct trust can be calculated based on their direct communication experiences. Based on trust evidence, trust attributes are calculated to obtain cloud drops, which can be used to establish direct trust cloud based on the backward cloud generator. If there is enough trust evidence between one-hop neighbor nodes, only direct trust is calculated for establishing a trust relationship.

Recommendation Trust Cloud Establishment

In UWSNs, underwater sensor nodes freely float with the ocean currents. The neighborhood of a sensor node changes from time to time. In the same neighborhood, the trust values can share with each other to provide recommendation. In a different neighborhood, the trust values can be transferred to obtain indirect trust. For neighbor nodes without direct communication or enough direct interaction experiences, recommendations from other nodes are required to establish recommendation trust. The nodes that provide recommendations are called recommenders. The calculated direct trusts stored in the recommender are used for recommendations. Then direct and recommendation trusts are combined as the current trust cloud.

Indirect Trust Cloud Establishment

For multihop nodes, indirect trust clouds are established. In the process of trust transfer, in order to limit the path length of trust transfer and save energy consumption of recommendation, the six degrees of separation rule is adopted to establish recommendation links, which are used to transfer commendations for multihop sensor nodes.

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Detection rate of malicious nodes (%)

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Figure 4. Comparison of the expected trust value.

Trust Judgment and Evaluation The trust model is used to quantify trust relationships between sensor nodes. Based on the quantified results and similarity judgment, a sensor node can judge whether another node is trustworthy or not. Thus, in the operation of the UWSN, only trusty nodes are selected to use for data transmission.

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Trust Cloud Update Trust has two aspects of dynamic characteristics: first, the trust relationship between sensor nodes changes in a dynamic environment; second, trust decays over time. In the first case, due to dynamic ocean currents and unstable acoustic communication, the sensor nodes with interrupted communication due to an intermittently connected acoustic link may easily be mistaken as malicious nodes. Thus, a redemption factor is introduced to control normal communication. If the value of a sensor node’s trust is lower than the defined redemption factor, it will be forbidden from participating in communication until its trust value returns to normal. The redemption factor can also be used to avoid the excluded malicious nodes quickly joining the network again. In the second case, a memory factor is introduced to describe the time decay characteristic. The trust trails off along with time; thus, the trust closer to the current time can be given a higher weight, and the historical trust is assigned with a lower weight.

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The simulation is conducted using the Matlab simulator. The number of normal sensor nodes is 100. The number of malicious nodes ranges from 10 to 100 with an increment of 10 each time. All the sensor nodes are randomly deployed over a network size of 500 m  500 m. The transmission radius of each node is set as 50 m. We evaluate the performance of TCM in the following three aspects: 1. The performance of malicious node detection 2. The performance of trust value calculation 3. The performance of data transmission

The Performance of Malicious Node Detection

The detection rate and false alarm rate of malicious nodes are compared in Figs. 4a and 4b, respectively. TCM outperforms CBTM and LCT because CBTM and LCT do not take timeliness of trust into account. In addition, using the direct average method to evaluate trust values of sensor nodes is not reasonable.

The Performance of Trust Value Calculation

In order to evaluate the performance of TCM, four kinds of sensor nodes (normal nodes, malicious nodes, hypocritical nodes, and malicious recommender) are simulated. The communication behaviors of normal nodes have been good; thus, their trust values gradually rise along simulation time. On the contrary, malicious nodes’ trust values rapidly decrease with communication time. This is because malicious nodes lose many packets, which introduces unsuccessful communication. As shown in Fig. 5, even they stop performing malicious behaviors; it is hard for them to obtain high trust values. Hypocritical nodes first pretend to be normal nodes, and thus they are


The Performance of Data Transmission

As shown in Fig. 6, in the same environment, the rate of successful communication under TCM is higher than that of the other two trust models. The rate of successful communication decreases with the increasing number of malicious nodes. However, TCM can be robust against malicious attacks. When the ratio of malicious nodes grows from 10 to 50 percent, we can get a higher communication success rate and the trend is relatively flat, indicating that TCM can suppress a certain number of malicious nodes.

Conclusion

In this article, we have presented a detailed survey of existing trust management mechanisms. Since UWSNs possess specific characteristics, we introduce a new trust cloud model that is suitable for trust management in UWSNs. The research of trust management for UWSNs is still in its infancy; there are still many challenges remaining wide open for future investigation: 1. The challenge of underwater acoustic communication. The underwater environment is complex and dynamic, which leads to poor stability of underwater acoustic communication. How to effectively obtain trust evidence and accurately evaluate trust values is one of the key issues in trust management mechanisms. In addition, the instability of underwater acoustic communication makes UWSNs more vulnerable to various types of network attacks, which brings new challenges for network security. 2. The challenge of node mobility. In UWSNs, the positions of sensor nodes change with ocean water flow. Thus, communication behaviors and trust relationships between sensor nodes also constantly change, which causes a lot of difficulties for trust assessment. 3. The challenge of sparse node deployment. UWSNs are always used to monitor a large-scale scenario, and sensor nodes are usually sparsely deployed. In this case, sensor nodes are far away from each other, so the number of direct communication or information exchange between them is not enough to accurately evaluate trust relationship. How to carry out trust management under the condition of insufficient trust evidence becomes a new problem to be solved.

Acknowledgments

The work is supported by the Qing Lan Project and the National Natural Science Foundation of China under Grant No. 61572172 and No. 61602152, the Fundamental Research Funds for the Central Universities, No. 2016B10714 and No. 2016B03114, the Changzhou Sciences and Technology Program, No.CE20165023 and No.CE20160014, and the Six talent peaks project in Jiangsu Province, No.XYDXXJS-007. This research is also supported by a strategic research grant from City University of Hong


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evaluated with higher trust values. However, their trust values will rapidly decrease once they launch malicious attacks. The malicious recommender can be detected and will not continue to be used in the following trust evaluation. These results meet the requirements of slow establishment and rapid destruction of trust evaluation.

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Figure 6. Comparison of the successful communication rate. Kong, No. 7004615. This work has been supported by National Funding from the FCT — Fundação para a Ciência e a Tecnologia through the UID/EEA/500008/2013 Project, by the Government of Russian Federation, Grant 074U01, and by Finep, with resources from Funttel, Grant No. 01.14.0231.00, under the Radiocommunication Reference Center (Centro de Referência em Radiocomunicações — CRR) project of the National Institute of Telecommunications (Instituto Nacional de Telecomunicações — Inatel), Brazil.

References

[1] G. Han et al., “Secure Communication for Underwater Acoustic Sensor Networks,” IEEE Commun. Mag., vol. 53, no. 8, Aug. 2015, pp. 54–60. [2] G. Han et al., “Routing Protocols for Underwater Wireless Sensor Networks,” IEEE Commun. Mag., vol.53, no. 11, Nov. 2015, pp. 72–78. [3] G. Han et al., “An Attack-Resistant Trust Model Based on Multidimensional Trust Metrics in Underwater Acoustic Sensor Networks,” IEEE Trans. Mobile Comp., vol. 14, no. 12, 2015, pp. 2447–59. [4] A. Josang, R. Hayward, and S. Pope, “Optimal Trust Network Analysis with Subjective Logic,” Int’l. Conf. Emerging Security Info., Systems and Technologies, 2008, pp. 179–84. [5] Y. Ren et al., “A Novel Approach to Trust Management in Unattended Wireless Sensor Networks,” IEEE Trans. Mobile Comp., vol. 13, no. 7, 2014, pp.1409–23. [6] S. Ganeriwal, L. K. Balzan, and M. B. Srivastava, “Reputation-Based Framework for High Integrity Sensor Networks,” ACM Trans. Sensor Networks, 2004, pp. 66–77. [7] G. Crosby et al., “A Framework for Trust-Based Cluster Head Election in Wireless Sensor Networks,” 2nd IEEE Wksp. DSSNS, 2006, pp. 13–22. [8] C. Chen, R. Wang, and L. Zhang, “The Research of Subjective Trust Model Based on Fuzzy Theory in Open Networks,” Acta Electronica Sinica, vol. 11, 2010, pp. 2505–09. [9] R. Feng et al., “Trust Evaluation Algorithm for Wireless Sensor Networks Based on Node Behaviors and D-S Evidence Theory,” Sensors, vol. 11, no. 2, 2011, pp. 1345–60. [10] Y. Sun et al., “Information Theoretic Framework of Trust Modeling and Evaluation for Ad Hoc Networks,” IEEE JSAC, vol. 24, no. 2, 2006, pp. 305–17. [11] H. Dai, Z. Jia, and X. Dong, “An Entropy-Based Trust Modeling and Evaluation for Wireless Sensor Networks,” ICESS, 2008, pp. 27–34. [12] L. Zhang et al., “Dynamic Trust Model Based on Recommendation Chain Classification in Complex Network Environment,” J. Commun., vol. 36, no. 9, 2015, pp. 55–64. [13] D. Li, H. Meng, and X. Shi, “Membership Clouds and Membership Cloud Generators,” J. Comp. R&D, vol. 32, no. 6, 1995, pp. 15–20.

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[14] B. Ma, “Cross-Layer Trust Model and Algorithm of Node Selection in Wireless Sensor Networks,” ICCSN, 2009, pp. 812–15. [15] X. Xu et al., “Representation for Uncertainty Trust of WSN Based on Lightweight-Cloud,” J. Commun., vol. 35, no. 2, 2014, pp. 63–69.

Biographies

Jinfang Jiang ([email protected]) is currently a lecturer with the Department of Information & Communication Systems, Hohai University, Changzhou, China. She received her Ph.D. degree from the Department of Computer Science from Hohai University in 2015. Her current research interests are security, localization, and routing for sensor networks. Guangjie Han [S’01, M’05] ([email protected]) is currently a professor in the Department of Information & Communication System at Hohai University. He finished his work as a postdoctoral researcher with the Department of Computer Science at Chonnam National University, Korea, in 2008. He received his Ph.D degree in the Department of Computer Science from Northeastern University, Shenyang, China, in 2004. He has served as an Editor of IEEE Access and Telecommunication Systems. C hunsheng Z hu [S’12] ([email protected]) is a postdoctoral research fellow in the Department of Electrical and Computer Engineering, University of British Columbia, Canada. He received his Ph.D. degree in electrical and computer engineer-

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ing from the University of British Columbia in 2016. His current research interests mainly include wireless sensor networks, cloud computing, the Internet of Things, social networks, and security. Sammy Chan [S’87, M’89] ([email protected]) received his B.E. and M.Eng.Sc. degrees in electrical engineering from the University of Melbourne, Australia, in 1988 and 1990, respectively, and his Ph.D. degree in communication engineering from the Royal Melbourne Institute of Technology, Australia, in 1995. From 1989 to 1994, he was with Telecom Australia Research Laboratories, first as a research engineer, and between 1992 and 1994 as a senior research engineer and project leader. Since December 1994, he has been with the Department of Electronic Engineering, City University of Hong Kong, where he is currently an associate professor. Joel J. P. C.Rodrigues [S’01, M’06, SM’06] ([email protected]) is a professor at the Inatel, Brazil and senior researcher at IT, Portugal. He has been a professor at UBI, Portugal, and a visiting professor at UNIFOR. He is the leader of the NetGNA Research Group (http://netgna.it.ubi.pt), the President of the scientific council at ParkUrbis ®C Covilhã Science and Technology Park, the Past-Chair of the IEEE ComSoc TCs on eHealth and Communications Software, and a Steering Committee member of the IEEE Life Sciences Technical Community. He is the Editor-inChief of three international journals and an Editorial Board member of several journals. He has authored or coauthored over 500 papers in refereed international journals and conferences, 3 books, and two patents.
