Grammati E. Pantziou

A survey on jamming attacks and countermeasures in WSNs

Jamming represents the most serious security threat in the field of wireless sensor networks (WSNs), as it can easily put out of order even WSNs that utilize strong highlayer security mechanisms, simply because it is often ignored in the initial WSN design. The objective of this article is to provide a general overview of the critical issue of jamming in WSNs and cover all the relevant work, providing the interested researcher pointers for open research issues in this field. We provide a brief overview of the communication protocols typically used in WSN deployments and highlight the characteristics of contemporary WSNs, that make them susceptible to jamming attacks, along with the various types of jamming which can be exercised against WSNs. Common jamming techniques and an overview of various types of jammers are reviewed and typical countermeasures against jamming are also analyzed. The key ideas of existing security mechanisms against jamming attacks in WSNs are presented and open research issues, with respect to the defense against jamming attacks are highlighted.

Review: Mobile recommender systems in tourism

Recommender Systems (RSs) have been extensively utilized as a means of reducing the information overload and offering travel recommendations to tourists. The emerging mobile RSs are tailored to mobile device users and promise to substantially enrich tourist experiences, recommending rich multimedia content, context-aware services, views/ratings of peer users, etc. New developments in mobile computing, wireless networking, web technologies and social networking leverage massive opportunities to provide highly accurate and effective tourist recommendations that respect personal preferences and capture usage, personal, social and environmental contextual parameters. This article follows a systematic approach in reviewing the state-of-the-art in the field, proposing a classification of mobile tourism RSs and providing insights on their offered services. It also highlights challenges and promising research directions with respect to mobile RSs employed in tourism.

A Rendezvous-Based Approach Enabling Energy-Efficient Sensory Data Collection with Mobile Sinks

A large class of Wireless Sensor Networks (WSN) applications involve a set of isolated urban areas (e.g., urban parks or building blocks) covered by sensor nodes (SNs) monitoring environmental parameters. Mobile sinks (MSs) mounted upon urban vehicles with fixed trajectories (e.g., buses) provide the ideal infrastructure to effectively retrieve sensory data from such isolated WSN fields. Existing approaches involve either single-hop transfer of data from SNs that lie within the MSs range or heavy involvement of network periphery nodes in data retrieval, processing, buffering, and delivering tasks. These nodes run the risk of rapid energy exhaustion resulting in loss of network connectivity and decreased network lifetime. Our proposed protocol aims at minimizing the overall network overhead and energy expenditure associated with the multihop data retrieval process while also ensuring balanced energy consumption among SNs and prolonged network lifetime. This is achieved through building cluster structures consisted of member nodes that route their measured data to their assigned cluster head (CH). CHs perform data filtering upon raw data exploiting potential spatial-temporal data redundancy and forward the filtered information to appropriate end nodes with sufficient residual energy, located in proximity to the MSs trajectory. Simulation results confirm the effectiveness of our approach against as well as its performance gain over alternative methods.

A survey on algorithmic approaches for solving tourist trip design problems

The tourist trip design problem (TTDP) refers to a route-planning problem for tourists interested in visiting multiple points of interest (POIs). TTDP solvers derive daily tourist tours, i.e., ordered visits to POIs, which respect tourist constraints and POIs attributes. The main objective of the problem discussed is to select POIs that match tourist preferences, thereby maximizing tourist satisfaction, while taking into account a multitude of parameters and constraints (e.g., distances among POIs, visiting time required for each POI, POIs visiting days/hours, entrance fees, weather conditions) and respecting the time available for sightseeing on a daily basis. The aim of this work is to survey models, algorithmic approaches and methodologies concerning tourist trip design problems. Recent approaches are examined, focusing on problem models that best capture a multitude of realistic POIs attributes and user constraints; further, several interesting TTDP variants are investigated. Open issues and promising prospects in tourist trip planning research are also discussed.

Effective Determination of Mobile Agent Itineraries for Data Aggregation on Sensor Networks

A key feature of wireless sensor networks (WSNs) is the collaborative processing, where the correlation existing over the local data of sensor nodes (SNs) is exploited so that the total data volume can be reduced (data aggregation). The use of Mobile Agents (MAs), i.e., software entities able of migrating among nodes and resuming execution naturally, fits in this scenario; the local data of an SN can be combined with the data collected by an MA from other SNs in a way that depends on the specific program code of the MA. In this paper, we consider the problem of calculating near-optimal routes for MAs that incrementally aggregate the data as they visit the nodes in a distributed sensor network. Our algorithm follows a greedy-like approach always selecting the next node to be included in an itinerary in such a way that the cost of the so far formed itineraries is kept minimum at each step. Simulation results confirm the high effectiveness of the proposed algorithm as well as its performance gain over alternative approaches. Also, with the use of proper data structures, the computational complexity of the algorithm is kept low as it is formally proved in the paper.

Clustering in mobile ad hoc networks through neighborhood stability-based mobility prediction

Clustering for mobile ad hoc networks (MANETs) offers a kind of hierarchical organization by partitioning mobile hosts into disjoint groups of hosts (clusters). However, the problem of changing topology is recurring and the main challenge in this technique is to build stable clusters despite the host mobility. In this paper, we present a novel clustering algorithm, which guarantees longer lifetime of the clustering structure in comparison to other techniques proposed in the literature. The basis of our algorithm is a scheme that accurately predicts the mobility of each mobile host based on the stability of its neighborhood (i.e., how different is its neighborhood over time). This information is then used for creating each cluster from hosts that will remain neighbors for sufficiently long time, ensuring the formation of clusters that are highly resistant to host mobility. For estimating the future host mobility, we use provably good information theoretic techniques, which allow on-line learning of a reliable probabilistic model for the existing host mobility.

Computing shortest paths and distances in planar graphs

We provide here efficient sequential and parallel solutions to the following problem: given a planar digraph G (with real edge weights but no negative cycles) for preprocessing, answer on-line queries requesting the shortest distance (or path) between any two vertices in G. Our algorithms for preprocessing need O(n log n + q2) space and O(n log n + q2) sequential time. (Here q is the cardinality of a set of faces of a planar embedding of G that cover all vertices.)A parallel implementation on a CREW PRAM needs also O(n log n + q2) space and runs in O(log2n) time using O(n + M(q)) processors (where M(q) is the number of processors required to multiply two q × q matrices in O(log q) time), provided that the q faces are given by the input.This enables us to achieve O(log n) time using a single processor for a “distance” query, or O(L + log n) time for a “path” query (where L is the length of the path). Note that this is a considerable improvement over previous results in the case where q = o(n). Our techniques are based on the hammock decomposition of a planar digraph and the use of separators for computing quickly internal distances in the graph. Several other results are achieved. For outerplanar graphs, our algorithms preprocess the graph in O(n logn) space and run either in O(n log n) sequential time, or in O(log2n) time using O(n) processors on a CREW PRAM. A “distance” query can be answered in O(log n) time using a single processor. A “path” query is answered in O(L + log n) time. An optimal solution is given in the case of trees. We achieve O(1) time per “distance” query andwe need O(n) sequential time, or O(log n) time and O(n/log n) processors (on an EREW PRAM) for preprocessing. A “path” query is answered in O(L) time.

On-line and dynamic algorithms for shortest path problems

We describe algorithms for finding shortest paths and distances in a planar digraph which exploit the particular topology of the input graph. An important feature of our algorithms is that they can work in a dynamic environment, where the cost of any edge can be changed or the edge can be deleted. Our data structures can be updated after any such change in only polylogarithmic time, while a single-pair query is answered in sublinear time. We also describe the first parallel algorithms for solving the dynamic version of the shortest path problem.

Heuristics for the time dependent team orienteering problem

The Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW) can be used to model several real life problems. Among them, the route planning problem for tourists interested in visiting multiple points of interest (POIs) using public transportation. The main objective of this problem is to select POIs that match tourist preferences, taking into account a multitude of parameters and constraints while respecting the time available for sightseeing in a daily basis and integrating public transportation to travel between POIs (Tourist Trip Design Problem, TTDP). TDTOPTW is NP-hard while almost the whole body of the related literature addresses the non-time dependent version of the problem. The only TDTOPTW heuristic proposed so far is based on the assumption of periodic transit service schedules. Herein, we propose efficient cluster-based heuristics for the TDTOPTW which yield high quality solutions, take into account time dependency in calculating travel times between POIs and make no assumption on periodic service schedules. The validation scenario for our prototyped algorithms involved the transit network and real POI datasets compiled from the metropolitan area of Athens (Greece). Our TTDP algorithms handle arbitrary (i.e. determined at query time) rather than fixed start/end locations for derived tourist itineraries. Author-HighlightsTourist Trip Design Problem (TTDP): near-optimal multiple-day tourist tours maximizing tourist satisfaction (profit).Time Dependent Team Orienteering Problem with Time Windows (TDTOPTW): Modeling TTDP incorporating public transit transfers.First heuristic algorithms incorporating time dependency (with no assumption on periodicity) in travel costs.The start/end locations of any route are arbitrarily defined within the tourist destination area, at runtime.Testing on new TTDP-tailored benchmark instances based on POIs and the public transit network of Athens, Greece.

An approach for near-optimal distributed data fusion in wireless sensor networks

In wireless sensor networks (WSNs), a lot of sensory traffic with redundancy is produced due to massive node density and their diverse placement. This causes the decline of scarce network resources such as bandwidth and energy, thus decreasing the lifetime of sensor network. Recently, the mobile agent (MA) paradigm has been proposed as a solution to overcome these problems. The MA approach accounts for performing data processing and making data aggregation decisions at nodes rather than bring data back to a central processor (sink). Using this approach, redundant sensory data is eliminated. In this article, we consider the problem of calculating near-optimal routes for MAs that incrementally fuse the data as they visit the nodes in a WSN. The order of visited nodes (the agent’s itinerary) affects not only the quality but also the overall cost of data fusion. Our proposed heuristic algorithm adapts methods usually applied in network design problems in the specific requirements of sensor networks. It computes an approximate solution to the problem by suggesting an appropriate number of MAs that minimizes the overall data fusion cost and constructs near-optimal itineraries for each of them. The performance gain of our algorithm over alternative approaches both in terms of cost and task completion latency is demonstrated by a quantitative evaluation and also in simulated environments through a Java-based tool.