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In collaborative wireless networks with low infrastructure, the presence of misbehaving nodes can have a negative impact on network performance. In particular, we are interested in dealing with this nasty presence in road safety applications, based on vehicular ad hoc networks (VANETs). In this paper, we consider as harmful the presence of malicious nodes, which spread false and forged data, and selfish nodes, which cooperate only for their own benefit. To deal with this, we propose a distributed trust model (DTM^2), which is adapted from the job market signaling model. DTM^2 is based on allocating credits to nodes and securely managing these credits. To motivate selfish nodes to cooperate more, our solution establishes the cost of reception to access data, forcing them to earn credits. Moreover, to detect and exclude malicious nodes, DTM^2 requires the cost of sending, using signaling values inspired from economics and based on the node's behavior so that the more malicious a node is, the higher its sending cost, thus limiting their participation in the network. Similarly, rewards are given to nodes whose sent messages are considered truthful and that paid a sending cost considered correct. The latter is a guarantee for the receivers about the truthfulness of the message since, in the case of message refusal, the source node is not rewarded, despite its payment. We validated DTM^2 via a theoretical study using Markov chains and with a set of simulations in both urban and highway scenarios. Both theoretical and simulation results show that DTM^2 excludes from the network 100% of malicious nodes without causing any false-positive detection. Moreover, our solution guarantees a good ratio of reception, even in the presence of selfish nodes. [ABSTRACT FROM PUBLISHER]

We study information rates of time-varying flat-fading channels (FFC) modeled as finite-state Markov channels (FSMC). FSMCs have two main applications for FFCs: modeling channel error bursts and decoding at the receiver. Our main finding in the first application is that receiver observation noise can more adversely affect higher-order FSMCs than lower-order FSMCs, resulting in lower capacities. This is despite the fact that the underlying higher-order FFC and its corresponding FSMC are more predictable. Numerical analysis shows that at low to medium SNR conditions (SNR ≲ 12 dB) and at medium to fast normalized fading rates (0.01 ≲ fDT ≲ 0.10), FSMC information rates are non-increasing functions of memory order. We conclude that BERS obtained by low-order FSMC modeling can provide optimistic results. To explain the capacity behavior, we present a methodology that enables analytical comparison of FSMC capacities with different memory orders. We establish sufficient conditions that predict higher/lower capacity of a reduced-order FSMC, compared to its original high-order FSMC counterpart. Finally, we investigate the achievable information rates in FSMC-based receivers for FFCs. We observe that high-order FSMC modeling at the receiver side results in a negligible information rate increase for normalized fading rates fDT ≲ 0.01. [ABSTRACT FROM AUTHOR]

A call admission control scheme named thinning scheme, which smoothly throttles the admission rates of calls according to their priorities and aims to provide multiple prioritized traffic with a desired quality of service, is investigated under more general conditions. Using the theory of multidimensional Markov birth-death process, analytical formulas for call blocking probabilities are derived. [ABSTRACT FROM AUTHOR]

Presents information on a study which focused on a quality-based birth-and-death queueing model used for evaluating the performance of a code-division multiple access cellular mobile communication system. Performance analysis via quality-based birth-death queueing model; Numerical examples and discussions.

In this paper, we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: 1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low-level change information between the time layers and object-level building description to recognize and separate changed and unaltered buildings. 2) To answer the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature-based modules. 3) To simultaneously ensure the convergence, optimality, and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel nonuniform stochastic object birth process which generates relevant objects with higher probability based on low-level image features. [ABSTRACT FROM AUTHOR]