Azizur Rahim

A Comprehensive Survey of MAC Protocols for Wireless Body Area Networks

In this paper, we present a comprehensive study of Medium Access Control (MAC) protocols developed for Wireless Body Area Networks (WBANs). In WBANs, small battery operated on-body or implanted biomedical sensor nodes are used to monitor physiological signs such as temperature, blood pressure, ElectroCardioGram (ECG), ElectroEncephaloGraphy (EEG) etc. We discuss design requirements for WBANs with major sources of energy dissipation. Then, we further investigate the existing designed protocols for WBANs with focus on their strengths and weaknesses. Paper ends up with concluding remarks and open research issues for future work.

Adaptive Medium Access Control Protocol for Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are widely used for applications such as modern health-care systems, where wireless sensors (nodes) monitor the parameter(s) of interest. Nodes are provided with limited battery power and battery power is dependent on radio activity. MAC protocols play a key role in controlling the radio activity. Therefore, we present Adaptive Medium Access Control (A-MAC) protocol for WBANs supported by linear programming models for the minimization of energy consumption and maximization of dataflow. Our proposed protocol is adaptive in terms of guard band assignment technique and sleep/wakeup mechanism. We focus on specific application to monitor human body with the help of nodes which continuously scan body for updated information. If the current value is within normal range, nodes do not try to access channel. However, if the current value rises or falls beyond the permissible range, nodes switch on their transceiver to access channel. Moreover, A-MAC uses TDMA approach to access channel and well-defined synchronization scheme to avoid collisions. Furthermore, we conduct a comprehensive analysis supported by MATLAB simulations to provide estimation of delay spread. Simulation results justify that the proposed protocol performs better in terms of network lifetime and throughput as compared to the counterpart protocols.

Time-Location-Relationship Combined Service Recommendation Based on Taxi Trajectory Data

Recently, urban traffic management has encountered a paradoxical situation which is the empty carrying phenomenon for taxi drivers and the difficulty of taking a taxi for passengers. In this paper, through analyzing the quantitative relationship between passengers’ getting on and off taxis, we propose a time-location-relationship (TLR) combined taxi service recommendation model to improve taxi drivers’ profits, uncover the knowledge of human mobility patterns, and enhance passengers’ travel experience. Moreover, the TLR model uses Gaussian process regression and statistical approaches to acquire passenger volume, mean trip distance, and average trip time in functional regions during every period on weekdays and weekends, and allows drivers to pick up more passengers within a short time frame. Finally, we compare our proposed model with the autoregressive integrated moving average model, the back-propagation neural network model, the support vector machine model, and the gradient boost decision tree model by using the real taxi GPS data in Beijing. The experimental results show that our optimizing taxi service recommendation can predict more accurately than others by considering the 3-D properties.

A game-theoretic incentive scheme for social-aware routing in selfish mobile social networks

Cooperative data forwarding can improve the performance of data routing in Mobile Social Networks (MSNs). However, previous studies mainly assumed that mobile nodes show selfish behaviors in data relaying merely due to their limited device resources. Nevertheless, the observation of everyday experience infers that they mitigate their selfishness based on their social relationships and content knowledge to achieve their social objective, i.e., they are socially selfish (SS). Therefore, how to promote SS nodes to participate in data forwarding becomes peculiarly challenging in MSNs. In this paper, we propose Game-theoretic Incentive Scheme for Social-aware rOuting, namely GISSO, to stimulate SS nodes in message relaying and guarantee that the routing performance gets maximized when SS nodes follow the scheme. First, we identify the social utility of each message to an intermediate node based on the strength of her social ties and message properties. Then, we apply an alternating-offers bargaining game in which SS nodes trade their messages with the aim of maximizing their social utility. We not only use subgame perfect Nash equilibrium as the agreement of two players to prove the efficiency of our game but also extensively evaluate the performance of GISSO using simulations over two real datasets. The comparison of GISSO with some benchmark social-aware protocols illustrates that GISSO overcomes SS nodes and outperforms the other algorithms regarding message delivery ratio and delay while generates low communication cost. Socially selfish mobile nodes mitigate their selfishness level to achieve their social objectives.A bargaining-based incentive scheme to stimulate selfish nodes in cooperation is proposed.Two real-world datasets with contact and social information are employed to evaluate the system.Experiments analyze the schemes performance in delivery ratio, delay, and cost.

Vehicular Social Networks: A survey

Abstract A Vehicular Social Network (VSN) is an emerging field of communication where relevant concepts are being borrowed from two different disciplines, i.e., vehicular ad-hoc networks (VANETs) and mobile social networks (MSNs). This emerging paradigm presents new research fields for content sharing, data dissemination, and delivery services. Based on social network analysis (SNA) applications and methodologies, interdependencies of network entities can be exploited in VSNs for prospective applications. VSNs involve social interactions of commuters having similar objectives, interests, or mobility patterns in the virtual community of vehicles, passengers, and drivers on the roads. In this paper, considering social networking in a vehicular environment, we investigate the prospective applications of VSNs and communication architecture. VSNs benefit from the social behaviors and mobility of nodes to develop novel recommendation systems and route planning. We present a state-of-the-art literature review on socially-aware applications of VSNs, data dissemination, and mobility modeling. Further, we give an overview of different recommendation systems and path planning protocols based on crowdsourcing and cloud-computing with future research directions.

Mobility Dataset Generation for Vehicular Social Networks Based on Floating Car Data

Vehicular social networks (VSNs) have attracted the research community due to its diverse applications ranging from safety to entertainment. Social vehicles standing for private cars and floating cars standing for taxis are two important components of VSN. However, the lack of social vehicles data causes some factors are neglected including social aspects and macroscopic features, which blocks researching social attributes of vehicles in VSN. Generating a realistic mobility dataset for VSN validation has been a great challenge. In this paper, we present the detailed procedure to generate social vehicular mobility dataset from the view of floating car data, which has the advantage of wide universality. First, through the deep analysis and modeling of the dataset of floating cars and combining with the official data, we predict the origin–destination (OD) matrix of social vehicles with the gravity model, and then calibrate the OD matrix with the average growth factor method. Second, we construct network description after editing the road network. Third, we use simulation of urban mobility to reproduce the scenario in view of microsimulation by generating the mobility dataset of social vehicles based on floating car data and urban functional areas. At last, we prove the effectiveness of our method by comparing with real traffic situation in Beijing. The generated mobility model may not accurately represent the mobility of social vehicles in few spots, such as train station or airport, however, exploiting figures and facts of transportation in the city have been considered in the study to calibrate the model up to maximum possible realization.

Bio-inspired packet dropping for ad-hoc social networks

SUMMARY Ad-hoc social networks (ASNETs) explore social properties of nodes in communications. The usage of various social applications in a resource-scarce environment and the dynamic nature of the network create unnecessary congestion that might degrade the quality of service dramatically. Traditional approaches use drop-tail or random-early discard techniques to drop data packets from the intermediate node queue. Nonetheless, because of the unavailability of the social properties, these techniques are not suitable for ASNETs. In this paper, we propose a Bio-inspired packet dropping (BPD) algorithm for ASNETS. BPD imitates the matching procedure of receptors and epitopes in immune systems to detect congestions. The drop probability settings depend on the selection of data packets, which is based on node popularity level. BPD selects the most prioritized node through social properties, which is inspired by the B-cell stimulation in immune systems. To fairly prioritize data packets, two social properties are used: (1) similarity and (2) closeness centrality between nodes. Extensive simulations are carried out to evaluate and compare BPD to other existing schemes in terms of mean goodput, mean loss rate, throughput, delay, attained bandwidth, and overhead ratio. The results show that the proposed scheme outperforms these existing schemes. Copyright

Cooperative data forwarding based on crowdsourcing in vehicular social networks

Abstract The mobile crowdsourcing capabilities of commuters in Vehicular Social Networks (VSNs) can provide promising solutions to several challenges of today’s smart cities, such as traffic congestion control, traffic management, smart parking, and route recommendation. In VSNs, mobile crowdsourcing depends upon the cooperative data forwarding behavior of nodes. In most of the existing data forwarding protocols designed for VSNs and delay tolerant networks (DTNs), it is assumed that nodes actively participate and are fully cooperative to relay data for others. In the real world, the selfish behavior of nodes considerably degrades the performance of these protocols. The reason is those selfish nodes only cooperate to relay data for nodes with whom they have strong social ties or mutual interests. In this paper, we present a Cooperative Data Forwarding (CDF) mechanism to stimulate the selfish nodes to participate in data forwarding. To enhance data forwarding mechanism, CDF is based on a socially-aware routing mechanism and a cooperative algorithm using direct observations and mobile crowdsourcing information to stimulate selfish nodes to participate in data forwarding. Besides, CDF is a multi-hop single copy forwarding mechanism which considerably decreases the network overhead. In our experimental results and analysis, we use real-world vehicular mobility dataset based on mobile crowdsourcing. The results show that CDF encourages more and more nodes to cooperate and improves network performance significantly in terms of data delivery ratio, transmission cost, and end-to-end delay which can substantially enhance the mobile crowdsourcing applications of VSNs.

IS2Fun: Identification of Subway Station Functions Using Massive Urban Data

Urbanization and modernization accelerate the evolution of urban morphology with the formation of different functional regions. To develop a smart city, how to efficiently identify the functional regions is crucial for future urban planning. Differed from the existing works, we mainly focus on how to identify the latent functions of subway stations. In this paper, we propose a semantic framework (IS2Fun) to identify spatio-temporal functions of stations in a city. We apply the semantic model Doc2vec to mine the semantic distribution of subway stations based on human mobility patterns and points of interest (POIs), which sense the dynamic (people’s social activities) and static characteristics (POI categories) of each station. We examine the correlation between mobility patterns of commuters and travellers and the spatio-temporal functions of stations. In addition, we develop the POI feature vectors to jointly explore the functions of stations from a perspective of static geographic location. Subsequently, we leverage affinity propagation algorithm to cluster all the stations into ten functional clusters and obtain the latent spatio-temporal functions. We conduct extensive experiments based on the massive urban data, including subway smart card transaction data and POIs to verify that the proposed framework IS2Fun outperforms existing benchmark methods in terms of identifying the functions of subway stations.

IEEE 802.15.4 Based Adaptive MAC Protocols

WSANs provide the infrastructure for many applications of CPS. Lots of these applications use the IEEE 802.15.4 standard. However, it does not provide any means of differentiated services to improve QoS for time-critical and delay-sensitive events. A large amount of efforts have been made to address such issues. In this chapter, an overview on some interesting mechanisms used in existing adaptive and real-time protocols in compliance with IEEE 802.15.4 is presented. Careful examination of these research works reveals that by optimizing the original specifications and dynamically adjusting the protocol parameters, the total network efficiency can be significantly improved. Nevertheless, there are still certain challenges to overcome in pursuing the most appropriate protocol without introducing unacceptable side-effects.