Sensornet protocols periodically broadcast beacons for neighborhood information advertisement, but beacon transmissions are costly when power-saving radio duty cycling mechanisms are used. We show that piggybacking multiple beacons in a single transmission significantly reduces transmission costs and argue that this shows the need for a new layer in the sensornet stack—an announcement layer—that coordinates beacons across upper layer protocols. An announcement layer piggybacks beacons and coordinates their transmission so that the total number of transmissions is reduced. With an announcement layer, new or mobile nodes can quickly gather announcement information from all neighbors and all protocols by issuing an announcement pull operation. Likewise, protocols can quickly disseminate new announcement information to all neighbors by issuing an announcement push operation. We have implemented an announcement layer in the Contiki operating system and three data collection and dissemination protocols on top of the announcement layer. We show that beacon coordination both improves protocol performance and reduces power consumption.

Abstract—We consider the problem of neighbor discovery in wireless networks with nodes operating in multiple frequency bands and with asymmetric beacon intervals. This is a challenging task when considering such heterogenous operation conditions and when performed without any external assistance. We present linear programming (LP) optimization and two strategies, named OPT and SWOPT, allowing nodes performing fast, asynchronous, and passive discovery. Our optimization is slotted based and determines a listening schedule describing when to listen, for how long, and on which channel. We compare our strategies with the passive discovery of the IEEE 802.15.4 standard. The results confirm that our optimization improves the performance in terms of first, average, and last discovery time. I.

Abstract—It is generally foreseen that the number of wirelessly connected networking devices will increase in the next decades, leading to a rise in the number of applications involving largescale networks. A major building block for enabling self- * system properties in ad-hoc scenarios is the run-time discovery of neighboring devices and somewhat equivalently, the estimation of the local node density. This problem has been studied extensively before, mainly in the context of fully-connected, synchronized networks. In this paper, we propose a novel adaptive and decentralized solution, the NetDetect algorithm, to the problem of discovering neighbors in a dynamic wireless network. The main difference with existing state of the art is that we target dynamic scenarios, i.e., multihop mesh networks involving mobile devices. The algorithm exploits the beaconing communication mechanism, dynamically adapting the beacon rate of the devices in the network based on local estimates of neighbor densities. We evaluate NetDetect on a variety of networks with increasing levels of dynamics: fully-connected networks, static and mobile multi-hop mesh networks. Results show that NetDetect performs well in all considered scenarios, maintaining a high rate of neighbor discoveries and good estimate of the neighborhood density even in very dynamic situations. More importantly, the proposed solution is adaptive, tracking changes in the local environment of the nodes without any additional algorithmic reconfiguration. Comparison with existing approaches shows that the proposed scheme is efficient from both convergence time and energy perspectives. I.

Abstract—The density estimation of diverse sensor types in a heterogeneous sensor network is a useful and challenging service that can be applied in clustering schemes, node redeployment, and sleep mode scheduling. Energy efficiency is one of the main requirements for any wireless sensor network service. Besides, the service has to provide a fresh version of the estimation to each node. Network dynamics, especially node mobility, introduce new challenges. Moreover, churn makes the problem even more complicated. In this paper we introduce a gossip-based approach for the density estimation of sensor diversity in clustered dynamic networks. The devised method supports node mobility and churn, as well as redeployment of new nodes. It is fully distributed and adaptive to network dynamics. We analyze the effect of mobility as well as scalability in the number of clusters and the quantity of nodes. Our algorithm has a fast convergence speed and provides more accurate estimation compared to similar approaches. Index Terms—Density estimation, Heterogeneous sensor network, Ad-hoc mobile networks, Gossiping

Based on spatiotemporal properties of neighborhood and mobile properties of nodes in the networks, we propose Selective Reference Mechanism to trade off between the delay and overhead of neighbor discovery in low-duty-cycle WSNs. Extensive simulation and test-bed experiment confirm our theoretical analysis, showing as much as 35.4 % increase in discovery probability, 38.6 % reduction in discovery delay and 27.7 % reduction in total energy consumption while packet loss ratio of communication is set to be 10% and nodes of the networks move in walking speed.

Abstract—The need to monitor groups of mobile entities arises in many application contexts. Examples include the study of the social behavior of humans and wildlife, the shepherding of livestock, the care giving to people that are not self-sufficient. Human- or animal-borne wireless devices can be used to detect the joining or leaving of group members, even in infrastructureless scenarios. In this work, we apply wireless sensor networks devices to this problem that has hitherto received little attention. We analyze three points of the solution space. At one extreme, group membership information is proactively and collectively maintained by each node in the group. At the other extreme, the dissemination of group membership updates is triggered reactively by relying on a lower-level neighbor discovery protocol. In the middle lies a solution borrowing ideas from the two extremes. We compare our solutions through simulation of synthetic scenarios and real-world mobility traces of humans. I.

As a supporting primitive of many mobile device applications, neighbor discovery identifies nearby devices so that they can exchange information and collaborate in a peer-topeer manner. To date, discovery schemes trade a long latency for energy efficiency and require a collaborative duty cycle pattern, and thus they are not suitable for interactive mobile applications where a user is unable to configure others’ devices. In this paper, we propose Acc, which serves as an on-demand generic discovery accelerating middleware for many existing neighbor discovery schemes. Acc leverages the discovery capabilities of neighbor devices, supporting both direct and indirect neighbor discoveries. Our evaluations show that Acc-assisted discovery schemes reduce latency by a maximum of 51.8%, compared with the schemes consuming the same amount of energy. We further present and evaluate a Crowd-Alert application where Acc can be employed by taxi drivers to accelerate selection of a direction with fewer competing taxis and more potential passengers, based on a 10 GB dataset of more than 15,000 taxis in a metropolitan area.

Abstract—Wireless Sensor Networks have been used in many mobile applications such as wildlife tracking and participatory urban sensing. Because of the combination of high mobility and low-duty-cycle operations, it is a challenging issue to reduce discovery delay among mobile nodes, so that mobile nodes can establish connection quickly once they are within each other’s vicinity. Existing discovery designs are essentially pair-wise based, in which discovery is passively achieved when two nodes are pre-scheduled to wake-up at the same time. In contrast, for the first time, this work reduces discovery delay significantly by proactively referring wake-up schedules among a group of nodes. Because proactive references incur additional overhead, we introduce a novel selective reference mechanism based on spatiotemporal properties of neighborhood and the mobility of the nodes. Our quantitative analysis indicates that the discovery delay of our group-based mechanism is significantly smaller than that of the pair-wise one. Our testbed experiments using 40 sensor nodes confirm our theoretical analysis, showing one order of magnitude reduction in discovery delay compared with traditional pair-wise methods with only 0.5%∼8.8 % increase in energy consumption. I.