In this paper, we present a family of adaptive protocols, called SPIN (Sensor Protocols for Information via Negotiation), that efficiently disseminates information among sensors in an energy-constrained wireless sensor network. Nodes running a SPIN communication protocol name their data using high-level data descriptors, called meta-data. They use meta-data negotiations to eliminate the transmission of redundant data throughout the network. In addition, SPIN nodes can base their communication decisions both upon application-specific knowledge of the data and upon knowledge of the resources that are available to them. This allows the sensors to efficiently distribute data given a limited energy supply. We simulate and analyze the performance of two specific SPIN protocols, comparing them to other possible approaches and a theoretically optimal protocol. We find that the SPIN protocols can deliver 60% more data for a given amount of energy than conventional approaches. We also find that, in terms of dissemination rate and energy usage, the SPlN protocols perform close to the theoretical optimum.

/pdf/adaptive-protocols-for-information-dissemination-in-wireless-2lo594hki6.pdf

Adaptive protocols for information dissemination in wireless sensor networks

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

Topology Control (TC) is one of the most important techniques used in wireless ad hoc and sensor networks to reduce energy consumption (which is essential to extend the network operational time) and radio interference (with a positive effect on the network traffic carrying capacity). The goal of this technique is to control the topology of the graph representing the communication links between network nodes with the purpose of maintaining some global graph property (e.g., connectivity), while reducing energy consumption and/or interference that are strictly related to the nodes' transmitting range. In this article, we state several problems related to topology control in wireless ad hoc and sensor networks, and we survey state-of-the-art solutions which have been proposed to tackle them. We also outline several directions for further research which we hope will motivate researchers to undertake additional studies in this field.

/pdf/topology-control-in-wireless-ad-hoc-and-sensor-networks-3e17u396zt.pdf

Topology Control in Wireless Ad Hoc and Sensor Networks

In this paper, we present a family of adaptive protocols, called SPIN (Sensor Protocols for Information via Negotiation), that efficiently disseminate information among sensors in an energy-constrained wireless sensor network. Nodes running a SPIN communication protocol name their data using high-level data descriptors, called meta-data. They use meta-data negotiations to eliminate the transmission of redundant data throughout the network. In addition, SPIN nodes can base their communication decisions both upon application-specific knowledge of the data and upon knowledge of the resources that are available to them. This allows the sensors to efficiently distribute data given a limited energy supply. We simulate and analyze the performance of four specific SPIN protocols: SPIN-PP and SPIN-EC, which are optimized for a point-to-point network, and SPIN-BC and SPIN-RL, which are optimized for a broadcast network. Comparing the SPIN protocols to other possible approaches, we find that the SPIN protocols can deliver 60% more data for a given amount of energy than conventional approaches in a point-to-point network and 80% more data for a given amount of energy in a broadcast network. We also find that, in terms of dissemination rate and energy usage, the SPIN protocols perform close to the theoretical optimum in both point-to-point and broadcast networks.

/pdf/negotiation-based-protocols-for-disseminating-information-in-1i6jhgr0p8.pdf

Negotiation-based protocols for disseminating information in wireless sensor networks

This self-contained introduction to the distributed control of robotic networks offers a distinctive blend of computer science and control theory. The book presents a broad set of tools for understanding coordination algorithms, determining their correctness, and assessing their complexity; and it analyzes various cooperative strategies for tasks such as consensus, rendezvous, connectivity maintenance, deployment, and boundary estimation. The unifying theme is a formal model for robotic networks that explicitly incorporates their communication, sensing, control, and processing capabilities--a model that in turn leads to a common formal language to describe and analyze coordination algorithms.Written for first- and second-year graduate students in control and robotics, the book will also be useful to researchers in control theory, robotics, distributed algorithms, and automata theory. The book provides explanations of the basic concepts and main results, as well as numerous examples and exercises.Self-contained exposition of graph-theoretic concepts, distributed algorithms, and complexity measures for processor networks with fixed interconnection topology and for robotic networks with position-dependent interconnection topology Detailed treatment of averaging and consensus algorithms interpreted as linear iterations on synchronous networks Introduction of geometric notions such as partitions, proximity graphs, and multicenter functions Detailed treatment of motion coordination algorithms for deployment, rendezvous, connectivity maintenance, and boundary estimation

/pdf/distributed-control-of-robotic-networks-a-mathematical-1q6nfcvi2l.pdf

Distributed Control of Robotic Networks: A Mathematical Approach to Motion Coordination Algorithms

Algorithm-based fault tolerance has been proposed as a technique to detect incorrect computations in multiprocessor systems. In algorithm-based fault tolerance, processors produce data elements that are checked by concurrent error detection mechanisms. We investigate the efficacy of this approach for diagnosis of processor faults. Because checks are performed on data elements, the problem of location of data errors must first be solved. We propose a probabilistic model for the faults and errors in a multiprocessor system and use it to evaluate the probabilities of correct error location and fault diagnosis. We investigate the number of checks that are necessary to guarantee error location with high probability. We also give specific check assignments that accomplish this goal. We then consider the problem of fault diagnosis when the locations of erroneous data elements are known. Previous work on fault diagnosis required that the data sets produced by different processors be disjoint. We show, for the first time, that fault diagnosis is possible with high probability, even in systems where processors combine to produce individual data elements. >

Almost certain fault diagnosis through algorithm-based fault tolerance

We consider the problem of gathering identical, memoryless, mobile agents in one node of an anonymous graph. Agents start from different nodes of the graph. They operate in Look-Compute-Move cycles and have to end up in the same node. In one cycle, an agent takes a snapshot of its immediate neighborhood (Look), makes a decision to stay idle or to move to one of its adjacent nodes (Compute), and in the latter case makes an instantaneous move to this neighbor (Move). Cycles are performed asynchronously for each agent. The novelty of our model with respect to the existing literature on gathering asynchronous oblivious agents in graphs is that the agents have very restricted perception capabilities: they can only see their immediate neighborhood.

An initial configuration of agents is called gatherable if there exists an algorithm that gathers all the agents of the configuration in one node and keeps them idle from then on, regardless of the actions of the asynchronous adversary. (The algorithm can be even tailored to gather this specific configuration.) The gathering problem is to determine which configurations are gatherable and find a (universal) algorithm which gathers all gatherable configurations. We give a complete solution of the gathering problem for regular bipartite graphs. Our main contribution is the proof that the class of gatherable initial configurations is very small: it consists only of "stars" (an agent A with all other agents adjacent to it) of size at least 3. On the positive side we give an algorithm accomplishing gathering for every gatherable configuration.

Gathering asynchronous oblivious agents with local vision in regular bipartite graphs

We study the problem of the amount of information required to perform fast broadcasting in tree networks. The source located at the root of a tree has to disseminate a message to all nodes. In each round each informed node can transmit to one child. Nodes do not know the topology of the tree but an oracle knowing it can give a string of bits of advice to the source which can then pass it down the tree with the source message. The quality of a broadcasting algorithm with advice is measured by its competitive ratio: the worst case ratio, taken over n-node trees, between the time of this algorithm and the optimal broadcasting time in the given tree. Our goal is to find a trade-off between the size of advice and the best competitive ratio of a broadcasting algorithm for n-node trees. We establish such a trade-off with an approximation factor of One), for an arbitrarily small positive constant e. This is the first problem for which a trade-off between the amount of provided information and the efficiency of the solution is shown for arbitrary size of advice.

https://www.diag.uniroma1.it/~fusco/res/adviceTradeoffs_C.pdf

Trade-offs between the size of advice and broadcasting time in trees

We study the time needed for deterministic leader election in the $\mathcal{LOCAL}$ model, where in every round a node can exchange any messages with its neighbors and perform any local computations. The topology of the network is unknown and nodes are unlabeled, but ports at each node have arbitrary fixed labelings which, together with the topology of the network, can create asymmetries to be exploited in leader election. We consider two versions of the leader election problem: strong LE in which exactly one leader has to be elected, if this is possible, while all nodes must terminate declaring that leader election is impossible otherwise, and weak LE, which differs from strong LE in that no requirement on the behavior of nodes is imposed, if leader election is impossible.

We show that the time of leader election depends on three parameters of the network: its diameter D, its size n, and its level of symmetryλ, which, when leader election is feasible, is the smallest depth at which some node has a unique view of the network. It also depends on the knowledge by the nodes, or lack of it, of parameters D and n. Optimal time of weak LE is shown to be Θ(D+λ) if either D or n is known to the nodes. (If none of these parameters is known, even weak LE is impossible.) For strong LE, knowing only D is insufficient to perform it. If only n is known then optimal time is Θ(n), and if both n and D are known, then optimal time is Θ(D+λ).

https://www.diag.uniroma1.it/~fusco/res/Symmetry_S.pdf

Knowledge, level of symmetry, and time of leader election

We consider the problem of fault diagnosis in multiprocessor systems. Processors perform tests on one another; fault-free testers correctly identify the fault status of tested processors, while faulty testers can give arbitrary test results. Processors fail with arbitrary probabilities and all failures are independent. The goal is to identify correctly the status of all processors, based on the set of test results. A diagnosis algorithm is optimal if it has the highest probability of correctness (reliability) among all (deterministic) diagnosis algorithms. We give a fast diagnosis algorithm and prove its optimality for arbitrary values of failure probabilities. This is the first time that optimal diagnosis is given for systems without any assumptions on the behavior of faulty processors or on the values of failure probabilities. We also investigate locally optimal diagnosis algorithms: For any set of test results, they return the most probable configuration of faulty and fault-free processors that could yield it. We show a fast diagnosis which is always locally optimal. If all processors have failure probabilities smaller than 1/2 , a locally optimal diagnosis is proved to be optimal. However, if some processors have failure probabilities exceeding 1/2 , a locally optimal diagnosis need not have the highest reliability. We even show examples that it may have arbitrarily small reliability when the number of processors increases, while optimal reliability remains constant.

Andrzej Pelc

Papers

Almost certain fault diagnosis through algorithm-based fault tolerance

Gathering asynchronous oblivious agents with local vision in regular bipartite graphs

Trade-offs between the size of advice and broadcasting time in trees

Knowledge, level of symmetry, and time of leader election

Optimal diagnosis of heterogeneous systems with random faults