A collaborative trust management scheme for emergency communication using delay tolerant networks

Delay Tolerant Network (DTN) comprises of nodes with small and limited resources including power and memory capacity. We propose the use of DTN as an alternate means of communication for the dissemination of emergency information in a post-disaster evacuation operation. We investigate the performance of DTN in providing emergency communication support services under packet dropping attacks. We consider internally motivated attacks where the nodes that are part of the emergency rescue team are compromised with malicious behaviours thereby dropping packets to disrupt the message dissemination during the evacuation operation. A way to mitigating malicious behaviour and improve network performance of DTN is to use incentives in exchanging information between nodes. Unlike existing schemes, we consider the Basic Watchdog Detection System which detects and acts against misbehaving nodes to reduce their overall impact on the network performance. We design a Collaborative Trust Management Scheme (CTMS) which is based on the Bayesian detection watchdog approach to detect selfish and malicious behaviour in DTN nodes. We have evaluated our proposed CTMS through extensive simulations and compared our results with the other existing schemes. Our evaluations show that the use of adequate collaborative strategies between well behaved nodes could improve the performance of Watchdog schemes taking into account the delivery ratio, routing cost and the message delay from the source node to the destination node

A collaborative trust management scheme for emergency communication using delay tolerant networks

Delay Tolerant Network (DTN) comprises of nodes with small and limited resources including power and memory capacity. We propose the use of DTN as an alternate means of communication for the dissemination of emergency information in a post-disaster evacuation operation. We investigate the performance of DTN in providing emergency communication support services under packet dropping attacks. We consider internally motivated attacks where the nodes that are part of the emergency rescue team are compromised with malicious behaviours thereby dropping packets to disrupt the message dissemination during the evacuation operation. A way to mitigating malicious behaviour and improve network performance of DTN is to use incentives in exchanging information between nodes. Unlike existing schemes, we consider the Basic Watchdog Detection System which detects and acts against misbehaving nodes to reduce their overall impact on the network performance. We design a Collaborative Trust Management Scheme (CTMS) which is based on the Bayesian detection watchdog approach to detect selfish and malicious behaviour in DTN nodes. We have evaluated our proposed CTMS through extensive simulations and compared our results with the other existing schemes. Our evaluations show that the use of adequate collaborative strategies between well behaved nodes could improve the performance of Watchdog schemes taking into account the delivery ratio, routing cost and the message delay from the source node to the destination node

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Performance Assessment of Aggregation and Deaggregation Algorithms in Vehicular Delay-Tolerant Networks

Vehicular Delay-Tolerant Networks (VDTNs) are a new approach for vehicular communications where vehicles cooperate with each other, acting as the communication infrastructure, to provide low-cost asynchronous opportunistic communications. These communication technologies assume variable delays and bandwidth constraints characterized by a non-transmission control protocol/ internet protocol architecture but interacting with it at the edge of the network. VDTNs are based on the principle of asynchronous communications, bundleoriented communication from the DTN architecture, employing a store-carryand- forward routing paradigm. In this sense, VDTNs should use the tight network resources optimizing each opportunistic contact among nodes. At the ingress edge nodes, incoming IP Packets (datagrams) are assembled into large data packets, called bundles. The bundle aggregation process plays an important role on the performance of VDTN applications. Then, this paper presents three aggregation algorithms based on time, bundle size, and a hybrid solution with combination of both. Furthermore, the following four aggregation schemes with quality of service (QoS) support are proposed: 1) single-class bundle with N = M, 2) composite-class bundle with N = M, 3) single-class bundle with N > M, and 4) composite-class bundle with N > M, where N is the number of classes of incoming packets and M is the number of priorities supported by the VDTN core network. The proposed mechanisms were evaluated through a laboratory testbed, called VDTN@Lab. The adaptive hybrid approach and the composite-class schemes present the best performance for different types of traffic load and best priorities distribution, respectively

A Review of Privacy Preserving Techniques inWireless Sensor Network

This paper represents a review of privacy preserving techniques in wireless sensor network. Wireless sensor networks are not secure. To preserve privacy of wireless sensor network various techniques are discovered. A lot of work has been done to address challenges faced to preserve privacy of wireless sensor network. In this paper we represent a research on privacy preserving techniques used in location privacy, data privacy and network privacy. This paper should provide help for further research in privacy preservation in wireless sensor network. Keywords: Context privacy, data privacy, source location privac

A Review of Privacy Preserving Techniques in Wireless Sensor Network

This paper represents a review of privacy preserving techniques in wireless sensor network. Wireless sensor networks are not secure. To preserve privacy of wireless sensor network various techniques are discovered. A lot of work has been done to address challenges faced to preserve privacy of wireless sensor network. In this paper we represent a research on privacy preserving techniques used in location privacy, data privacy and network privacy. This paper should provide help for further research in privacy preservation in wireless sensor network. Keywords: Context privacy, data privacy, source location privac

Towards better social crisis data with HERMES: Hybrid sensing for EmeRgency ManagEment System

People involved in mass emergencies increasingly publish information-rich contents in online social networks (OSNs), thus acting as a distributed and resilient network of human sensors. In this work, we present HERMES, a system designed to enrich the information spontaneously disclosed by OSN users in the aftermath of disasters. HERMES leverages a mixed data collection strategy, called hybrid crowdsensing, and state-of-the-art AI techniques. Evaluated in real-world emergencies, HERMES proved to increase: (i) the amount of the available damage information; (ii) the density (up to 7x) and the variety (up to 18x) of the retrieved geographic information; (iii) the geographic coverage (up to 30%) and granularity