Several prospective applications on vehicular networks have been defined. Most applications rely on beaconing mechanisms to broadcast the presence and updated information of a vehicle to surrounding neighbors. However, due to the broadcasting nature, no acknowledgement mechanism is provided. Therefore, vehicles do not perceive beacon collision if two or more vehicles simultaneously broadcast the beacons. Consequently, vehicles miss updated information from their neighbors. In this paper, we propose V-DESYNC, an algorithm that distributively desynchronizes vehicles to broadcast beacons at different times based on only timing information. V-DESYNC is designed to avoid the beacon collision and tolerate the highly dynamic behavior of vehicular networks. Our evaluation results indicate that V-DESYNC can significantly reduce the number of beacon collisions without decreasing the beaconing rate on vehicular networks.

In wireless ad hoc networks, all nodes are routers that are required to forward packets for each other. Undoubtedly, nodes that forward more packets will run out of power before others will. To save energy, some nodes may become selfish and refuse to forward packets. As a result, the overall system performance will be degraded with the increased number of selfish nodes. This problem is more severe in networks that employ a wake-sleep schedule for energy savings (e.g., GAF-based networks). Even if a few of active nodes (leader nodes in GAF) are selfish, such a network may be virtually partitioned. Therefore, we propose two schemes to detect and to punish selfish nodes as well as to reduce the possibility of virtual network partition: SWD and GAFSWD. In SWD, only GAF-leader nodes are watchdogs. This watchdog concept is further developed into GAFSWD whereby non-leader nodes take turn to be additional watchdogs for detecting selfish nodes and to be candidates for alternative routes when needed. The results indicate that our schemes achieve significantly better fair ratio and delivery ratio while incurring small false conviction under investigated scenarios.

Several research efforts have abstracted wireless sensor networks as relational databases whose data can be easily queried by users. However, none of these works includes recursive query mechanisms and globally logic reasoning techniques necessary for powerful uses as in deductive databases. In this paper, we propose an approach for abstracting wireless sensor networks as deductive databases that consist of rules, predicates, and logic-based queries. Our approach enables globally logic reasoning and data relating on a query that can be recursive or non-recursive. Additionally, the approach is more convenient to users because the users no longer need to be concerned about local behaviors of each sensor node. We also present LogicQ, an underlying system for disseminating and processing logic-based queries as well as collecting data in an energy-efficient manner. LogicQ uses a data filtering and suppressing technique that directly corresponds to an injected logic-based query for energy savings. Our performance analysis indicates that LogicQ can handle logic-based queries efficiently in terms of completeness and soundness while minimizing the energy consumption.

Spatial programming (SP) is a space-aware programming model for outdoor distributed embedded systems. Central to SP are the concepts of space and spatial reference, which provide applications with a virtual resource naming in networks of embedded systems. A network resource is referenced using its expected physical location and properties. Together with other SP features, such as reference consistency and access timeout, they help programmers cope with highly dynamic network configurations in a network-transparent fashion. We present the SP design and its implementation using smart messages, a lightweight software architecture similar to mobile agents, that we developed for networks of embedded systems. We also describe the implementation and evaluation of a simple SP application over a testbed consisting of HP iPAQs running Linux and equipped with 802.11 cards for wireless communication. The experimental results indicate that SP is a viable programming model for outdoor distributed computing.

Smart messages (SMs) are dynamic collections of code and data that migrate to nodes of interest in the network and execute on these nodes. A key challenge in programming pervasive computing environments using SMs is the ability to route SMs to nodes named by content. This paper describes the SM self-routing mechanism, which allows SMs to route themselves at each hop in the path toward a node of interest. SM applications can choose among multiple content based routing algorithms, switch dynamically the routing algorithm in use, or even implement the best suited routing algorithm for their needs. The main benefits provided by self-routing are high flexibility and resilience to adverse network conditions. To demonstrate these benefits, we present proof-of-concept implementation, simulation results, and analysis for the SM self-routing mechanism using several content-based routing algorithms. We also show preliminary results for SM routing algorithms executed over our SM prototype implementation.

Advances in processor, memory, and radio technology enable small and cheap nodes capable of sensing, communication, and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. We explore the directed diffusion paradigm for such coordination. Directed diffusion is data-centric in that all communication is for named data. All nodes in a directed-diffusion-based network are application aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network (e.g., data aggregation). We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network analytically and experimentally. Our evaluation indicates that directed diffusion can achieve significant energy savings and can outperform idealized traditional schemes (e.g., omniscient multicast) under the investigated scenarios.

In-network data aggregation is essential for wireless sensor networks where energy resources are limited. In a previously proposed data dissemination scheme (directed diffusion with opportunistic aggregation), data is opportunistically aggregated at intermediate nodes on a low-latency tree. In this paper, we explore and evaluate greedy aggregation, a novel approach that adjusts aggregation points to increase the amount of path sharing, reducing energy consumption. Our preliminary results suggest that, under investigated scenarios, greedy aggregation can achieve up to 45% energy savings over opportunistic aggregation in high-density networks without adversely impacting latency or robustness.

In most distributed systems, naming of nodes for low-level communication leverages topological location (such as node addresses) and is independent of any application. In this paper, we investigate an emerging class of distributed systems where low-level communication does not rely on network topological location. Rather, low-level communication is based on attributes that are external to the network topology and relevant to the application. When combined with dense deployment of nodes, this kind of named data enables in-network processing for data aggregation, collaborative signal processing, and similar problems. These approaches are essential for emerging applications such as sensor networks where resources such as bandwidth and energy are limited. This paper is the first description of the software architecture that supports named data and in-network processing in an operational, multi-application sensor-network. We show that approaches such as in-network aggregation and nested queries can significantly affect network traffic. In one experiment aggregation reduces traffic by up to 42% and nested queries reduce loss rates by 30%. Although aggregation has been previously studied in simulation, this paper demonstrates nested queries as another form of in-network processing, and it presents the first evaluation of these approaches over an operational testbed.

Advances in processor, memory and radio technology will enable small and cheap nodes capable of sensing, communication and computation. Networks of such nodes can coordinate to perform distributed sensing of environmental phenomena. In this paper, we explore the directed diffusion paradigm for such coordination. Directed diffusion is datacentric in that all communication is for named data. All nodes in a directed diffusion-based network are application-aware. This enables diffusion to achieve energy savings by selecting empirically good paths and by caching and processing data in-network. We explore and evaluate the use of directed diffusion for a simple remote-surveillance sensor network.