BurstProbe: Debugging Time-Critical Data Delivery in Wireless Sensor Networks

In this paper we present BurstProbe, a new technique to accurately measure link burstiness in a wireless sensor network employed for time-critical data delivery. Measurement relies on shared probing slots that are embedded in the transmission schedule and used by nodes to assess link burstiness over time. The acquired link burstiness information can be stored in the node's flash memory and relied upon to diagnose transmission problems when missed deadlines occur. Thus, accurate diagnosis is achieved in a distributed manner and without the overhead of transmitting rich measurement data to a central collection point. For the purpose of evaluation we have implemented BurstProbe in the GinMAC WSN protocol and we are able to demonstrate it is an accurate tool to debug time-critical data delivery. In addition, we analyze the cost of implementingBurstProbe and investigate its effectiveness

Droplet: A New Denial-of-Service Attack on Low Power Wireless Sensor Networks

In this paper we present a new kind of Denial-of-Service attack against the PHY layer of low power wireless sensor networks. Overcoming the very limited range of jamming-based attacks, this attack can penetrate deep into a target network with high power efficiency. We term this the Droplet attack, as it attains enormous disruption by dropping small, payload-less frame headers to its victim's radio receiver, depriving the latter of bandwidth and sleep time. We demonstrate the Droplet attack's high damage rate to full duty-cycle receivers, and further show that a high frequency version of Droplet can even force nodes running on very low duty-cycle MAC protocols to drop most of their packets

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