As sensornets are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). Toward understanding the complexity of joint QoS and INP optimization, we study the problem of jointly optimizing packet packing (i.e., aggregating shorter packets into longer ones) and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints (in particular, maximum packet size and reaggregation tolerance) instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. To understand the impact of joint QoS and INP optimization on sensornet performance, we design a distributed, online protocol tPack that schedules packet transmissions to maximize the local utility of packet packing at each node. Using a testbed of 130 TelosB motes, we experimentally evaluate the properties of tPack. We find that jointly optimizing data delivery timeliness and packet packing and considering real-world aggregation constraints significantly improve network performance. Our findings shed light on the challenges, benefits, and solutions of joint QoS and INP optimization, and they also suggest open problems for future research.

We present techniques for synchronizing nodes that periodically broadcast content and presence updates to colocated nodes over an ad hoc network, where nodes may exhibit Byzantine malicious behavior. Instead of aligning duty cycles, our algorithms synchronize the periodic transmissions of nodes. This allows nodes to save battery power by switching off their network cards without missing updates from their neighbors. We propose several novel attack classes and show that they are able to disrupt synchronization even when launched by a single attacker. Finally, we devise a rating based algorithm (RBA) that rates neighbors based on the consistency of their behavior. By favoring well-behaved nodes in the synchronization process, we show that RBA quickly stabilizes the synchronization process and reduces the number of lost updates by 85 percent. Our evaluation also shows that all our algorithms are computationally efficient and, for the setup considered, extend the device lifetime by 30 percent over an always-on Wi-Fi scenario.

An important mechanism to conserve energy and extend the battery life of low-power wireless networks is to increase the sleep-cycle of the nodes. However, increasing sleep-cycles can have unintended consequences on the application performance. Therefore, it is important to understand the impact of various sleep-cycle parameters on the performance of any given application. In this paper, we analyze the relationship between the sleep-cycle period and the accuracy of the localization application. We show that the sleep-cycle period has an exponential relationship with many of the parameters that impact localization accuracy. In general, such relationships lend themselves especially well to energy efficient system-level design of location dependent applications. Finally, to demonstrate the validity of our analysis, using an IEEE 802.11 based localization system, we present a set of experimental results that show the relationships between the sleep-cycle and the measurements accuracies in the localization systems.

As sensornets are increasingly being deployed in mission-critical applications, it becomes imperative that we consider application QoS requirements in in-network processing (INP). Towards understanding the complexity of joint QoS and INP optimization, we study the problem of jointly optimizing packet packing (i.e., aggregating shorter packets into longer ones) and the timeliness of data delivery. We identify the conditions under which the problem is strong NP-hard, and we find that the problem complexity heavily depends on aggregation constraints (in particular, maximum packet size and re-aggregation tolerance) instead of network and traffic properties. For cases when the problem is NP-hard, we show that there is no polynomial-time approximation scheme (PTAS); for cases when the problem can be solved in polynomial time, we design polynomial time, offline algorithms for finding the optimal packet packing schemes. To understand the impact of joint QoS and INP optimization on sensornet performance, we design a distributed, online protocol \emph{tPack} that schedules packet transmissions to maximize the local utility of packet packing at each node. Using a testbed of 130 TelosB motes, we experimentally evaluate the properties of tPack. We find that jointly optimizing data delivery timeliness and packet packing significantly improve network performance. Our findings shed light on the challenges, benefits, and solutions of joint QoS and INP optimization, and they also suggest open problems for future research.

In this paper, we present techniques for synchronizing nodes that periodically broadcast content and presence updates to co-located nodes over an ad-hoc network, where nodes may exhibit Byzantine malicious behavior. We first propose an algorithm for synchronizing the periodic transmissions of all the nodes in an attacker-free multi-hop network. This allows nodes to save battery power by switching off their network cards without missing updates from their neighbors. We then introduce a suite of spoofing attacks and show that they are able to disrupt synchronization and destabilize the network even when launched by a single attacker in large, multi-hop networks. Finally, we devise a rating based algorithm that rates neighbors based on the consistency of their behaviors. By favoring well-behaved nodes in the synchronization process, we show that we can address the issue of Byzantine malicious behavior very effectively. Our evaluation shows that the algorithms are computationally efficient and, for the setup considered, extend the device lifetime by 30% over an always-on Wi-Fi scenario. Moreover, in the presence of attacks, our rating based algorithm quickly stabilizes the synchronization process and reduces the number of lost updates by 85%.

Many real-world applications for sensor networks require event sampling with sufficient resolution over both spatial and temporal dimensions. When the deployed nodes are insufficient to fully cover the sensor field to satisfy this spatiotemporal sampling requirements of all events, nodes with intelligent mobility are crucial to improve the sampling quality. We measure the sampling quality using a utility function that incorporates both the importance and spatiotemporal properties of events. We then describe a Utility Driven Mobility (UDM) scheme, which enables autonomous mobility scheduling of nodes in a distributed fashion. We evaluate the performance of UDM via extensive simulation studies. Our work provides a practical framework for spatiotemporal sampling using mobile sensor networks.

Reprogramming over the network is an essential requirement in large-scale, long term wireless sensor network deployments. In this paper, we present LACONIC, a history-based code dissemination technique, for programmable wireless sensor networks supporting multiple applications. In order to attain network traffic abatement and timely code delivery, LACONIC exploits (1) application calling history and (2) code dissemination history. The application calling history is modeled by a Application Call Graph(ACG) which represents the calling relationships among multiple applications. Second, the code dissemination history as a set of observed previous code forwarding paths indicates previous code forwarding path of the associated application in the context of the application calling history. Therefore, the code request triggered by a requester is reached to the responder by traveling along the path, not being consecutively flooded. Using a flooding-based existing code dissemination work as the baseline, we show the effectiveness of Laconic in terms of network traffic and time delay through both probability model-based analysis and simulation results.

It is vital to support concurrent applications sharing a wireless sensor network in order to reduce the deployment and administrative costs, thus increasing the usability and efficiency of the network. We describe Melete 1 , a system that supports concurrent applications with efficiency, reliability, flexibility, programmability, and scalability. Our work is based on the Maté virtual machine [1] with significant modifications and enhancements. Melete enables reliable storage and execution of concurrent applications on a single sensor node. Dynamic grouping is used for flexible, on-the-fly deployment of applications based on contemporary status of the sensor nodes. The grouping procedure itself is programmed with the TinyScript language. A group-keyed code dissemination mechanism is also developed for reliable and efficient code distribution among sensor nodes. Both analytical and simulation results are presented to study the impact of several key parameters and optimization techniques on the code dissemination mechanism. Simulation results indicate satisfactory scalability of our techniques to both application code size and node density. The usefulness and effectiveness of Melete is also validated by empirical study.