A Hybrid Secure Scheme for Wireless Sensor Networks against Timing Attacks Using Continuous-Time Markov Chain and Queueing Model

Wireless sensor networks (WSNs) have recently gained popularity for a wide spectrum of applications. Monitoring tasks can be performed in various environments. This may be beneficial in many scenarios, but it certainly exhibits new challenges in terms of security due to increased data transmission over the wireless channel with potentially unknown threats. Among possible security issues are timing attacks, which are not prevented by traditional cryptographic security. Moreover, the limited energy and memory resources prohibit the use of complex security mechanisms in such systems. Therefore, balancing between security and the associated energy consumption becomes a crucial challenge. This paper proposes a secure scheme for WSNs while maintaining the requirement of the security-performance tradeoff. In order to proceed to a quantitative treatment of this problem, a hybrid continuous-time Markov chain (CTMC) and queueing model are put forward, and the tradeoff analysis of the security and performance attributes is carried out. By extending and transforming this model, the mean time to security attributes failure is evaluated. Through tradeoff analysis, we show that our scheme can enhance the security of WSNs, and the optimal rekeying rate of the performance and security tradeoff can be obtained. View Full-Tex

Balanced Multi-Channel Data Collection in Wireless Sensor Networks

Data collection is an essential task in Wireless Sensor Networks (WSNs). In data collection process, the sensor nodes transmit their readings to a common base station called Sink. To avoid a collision, it is necessary to use the appropriate scheduling algorithms for data transmission. On the other hand, multi-channel design is considered as a promising technique to reduce network interference and latency of data collection. This technique allows parallel transmissions on different frequency channels, thus time latency will be reduced. In this paper, we present a new scheduling method for multi-channel WSNs called Balanced Multi Channel Data Collection (Balanced MC-DC) Algorithm. The proposed protocol is based on using both Non-Overlapping Channels (NOC) and Partially Overlapping Channels (POC). It uses a new approach that optimizes the processes of tree construction, channel allocation, transmission scheduling and balancing simultaneously. Extensive simulations confirm the superiority of the proposed algorithm over the existing algorithms in wireless sensor networks

A Hybrid based Distributed Slot Scheduling Approach for WSN MAC

In Wireless Sensor Networks(WSNs), collision handling during transmission of data is an important challenge. MAC protocol plays a vital role in handling those collisions. Among different types of MAC protocols, schedule based MAC protocol is one where a valid schedule is prepared to handle the collision. The existing schedule based MAC protocols focus on preparing either a feasible schedule or an optimal schedule. In order to satisfy both feasibility as well as optimality feature, in this paper, we proposed a hybrid approach for slot scheduling that prepares a feasible schedule in a distributed manner and at the same time reduces the number of slots in the feasible schedule to achieve optimality. In this paper, we named this as Hybrid based Distributed Slot Scheduling (HDSS) approach. The proposed HDSS algorithm initially prepares a feasible schedule which is further tuned in quick time to prepare a valid schedule with a reduced number of slots. The reduction of the number of slots in the schedule improves the efficiency of data transmission in terms of latency. The simulation results show that the HDSS algorithm outperforms RD-TDMA with respect to both the number of slots allotted for a feasible schedule as well as the data transmission latency

Optimal Schedules for Data Gathering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are widely used for target monitoring: sensors monitor a set of targets, and forward the collected or aggregated data using multi-hop routing to the same location, called the sink. The resulting communication scheme is called ConvergeCast or Aggregated ConvergeCast. Several researchers studied the ConvergeCast and the Aggregated ConvergeCast, as to produce the shortest possible schedule that conveys all the packets or a packet aggregation to the sink. Nearly all proposed methods proceed in two steps, first the routing, and then the scheduling of the packets along the routes defined in the first step. The thesis is organized around four contributions. The first one is an improvement of the previous mathematical models that outputs (minimum-sized) multi-set of transmission configurations (TCs), in which a transmission configuration is defined as a set of links that can transmit concurrently. Our model allows the transmission of several packets per target, in both single-path and multi-path settings; we give two new heuristics for generating new improved transmission configurations. While such models go beyond the routing step, they do not specify an ordering over time of the configurations. Consequently, the second contribution consists of several algorithms, one exact and several heuristics, for ordering the configurations. Our results show that the approach of scheduling when restricted to a tree generated by the first contribution significantly outperforms the ordering of configurations of TC-approach for single-rate, single packet per sensor traffic patterns, but the TC approach gives better results for multi-rate traffic and when there are a large number of packets per sensor. In the last two contributions, we propose an exact mathematical model that takes care, in a single phase, of the routing and the scheduling, for the ConvergeCast and the aggregated ConvergeCast problem. They both correspond to decomposition models in which not only we generate transmission configurations, but an ordering of them. We performed extensive simulations on networks with up to 70 sensors for both ConvergeCast and Aggregated ConvergeCast, and compared our one phase results with one of the best heuristics in the literature