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Input traffic from Internet of Things (IoT) devices is often both periodic and requires to be received by a given deadline. This can create congestion at instants of time when traffic flowing from multiple devices arrives at a shared input port or gateway, resulting in missed deadlines at the receiver.As a consequence, scheduling techniques such as the “Earliest Deadline First” (EDF) and “Priority based on Average Load” (PAL) are used to schedule the flow from different devices so as to try to satisfy the needs of the largest number of traffic flows in a timely fashion. In this paper, we propose the Randomization of flow Generation Times (RGT) in order to smooth the total incoming traffic to the input port or gateway, on top of the use of EDF and PAL. We then evaluate the performance of RGT together with PAL and EDP, for traffic load with a varyingnumber of up to 6400 IoT devices. Our simulation results show that RGT provides significantly better performance when added to EDF and PAL. Also, the additional computation required by RGT at each device can be quite small, suggesting that RGT is a very useful addition for improving the performance of IoT networks.

The power needs of digital devices, their installation in locations where it is difficult to connect them to the power grid and the difficulty of frequently replacing batteries, create the need to operate digital systems with harvested energy. In such cases, local storage batteries must overcome the intermittent nature of the energy supply. System performance then depends on the intermittent energy supply, possible energy leakage, and system workload. Queueing networks with product-form solution (PFS) are standard tools for analyzing the performance of interconnected systems, and predicting relevant performance metrics including job queue lengths, throughput, and system turnaround times and delays. However, existing queueing network models assume unlimited energy availability, whereas intermittently harvested energy can affect system performance due to insufficient energy supply. Thus, this paper develops a new PFS for the joint probability distribution of energy availability, and job queue length for an N-node tandem system. Such models can represent production lines in manufacturing systems, supply chains, cascaded repeaters for optical links, or a data link with multiple input data ports that feeds into a switch or server. Our result enables the rigorous computation of the relevant performance metrics of such systems operating with intermittent energy.

Mobile networks are vulnerable to signalling attacks and storms that are caused by traffic patterns that overload the control plane, and differ from distributed denial of service (DDoS) attacks in the Internet since they directly attack the control plane, and also reserve wireless bandwidth without actually using it. Such attacks can result from malware and mobile botnets, as well as from poorly designed applications, and can cause service outages in 3G and 4G networks which have been experienced by mobile operators. Since the radio resource control (RRC) protocol in 3G and 4G networks is particularly susceptible to such attacks, we analyze their effect with a mathematical model that helps to predict the congestion that is caused by an attack. A detailed simulation model of a mobile network is used to better understand the temporal dynamics of user behavior and signalling in the network and to show how RRC based signalling attacks and storms cause significant problems in the control plane and the user plane of the network. Our analysis also serves to identify how storms can be detected, and to propose how system parameters can be chosen to mitigate their effect., Submitted to the IEEE TETC special issue on "Emerging topics in Cyber Security"

Date of Conference: 26-28 September 2011 Conference name: Computer and Information Sciences II - 26th International Symposium on Computer and Information Sciences To deal with increasing size and complexity, component-based software development has been employed in embedded systems. Due to several faults, components can make wrong assumptions about the working mode of the system and the working modes of the other components. To detect mode inconsistencies at runtime, we propose a "lightweight" error detection mechanism, which can be integrated with component-based embedded systems. We define links among three levels of abstractions: the runtime behavior of components, the working mode specifications of components and the specification of the working modes of the system. This allows us to detect the user observable runtime errors. The effectiveness of the approach is demonstrated by implementing a software monitor integrated into a TV system. © 2012 Springer-Verlag London Limited.

Disasters are often accompanied by damage to critical infrastructure, including (wireless) communications infrastructure. Our solution for emergency communications is based on advanced networks: Generalized Access Networks (GANs), Body Area Networks (BANs) and Vehicular Networks, to support dynamic, resilient communication services for disaster management.

Selection hyper-heuristics perform search over the space of heuristics by mixing and controlling a predefined set of low level heuristics for solving computationally hard combinatorial optimisation problems. Being reusable methods, they are expected to be applicable to multiple problem domains, hence performing well in cross-domain search. HyFlex is a general purpose heuristic search API which separates the high level search control from the domain details enabling rapid development and performance comparison of heuristic search methods, particularly hyper-heuristics. In this study, the performance of six previously proposed selection hyper-heuristics are evaluated on three recently introduced extended HyFlex problem domains, namely 0–1 Knapsack, Quadratic Assignment and Max-Cut. The empirical results indicate the strong generalising capability of two adaptive selection hyper-heuristics which perform well across the ‘unseen’ problems in addition to the six standard HyFlex problem domains.

Being able to reliable estimate the occupancy of areas inside a building can prove beneficial for managing an emergency situation, as it allows for more efficient allocation of resources such as emergency personnel. In indoor environments, however, occupancy detection can be a very challenging task. A solution to this can be provided by the use of Bluetooth Low Energy (BLE) beacons installed in the building. In this work we evaluate the performance of a BLE based occupancy detection system geared towards emergency situations that take place inside buildings. The system is composed of BLE beacons installed inside the building, a mobile application installed on occupants' mobile phones and a remote control server. Our approach does not require any processing to take place on the occupants' mobile phones, since the occupancy detection is based on a classifier installed on the remote server. Our real-world experiments indicated that the system can provide high classification accuracy for different numbers of installed beacons and occupant movement patterns.

We introduce a novel access control mechanism in order to safeguard privacy of medical data of patients in dynamic environments. Our access control model takes advantages from role-based access control (RBAC) and criticality aware access control (CAAC). In this way, our original approach allows the medical professionals with different roles to be granted access to medical records of patients automatically and without explicit request in case of a medical emergency. In this context, we design secure and privacy aware protocols from initial login to patients' medical data transmission and retrieval by the medical professionals. Moreover, we formally define access control policies for our system. Finally we show the feasibility of our approach by implementation and performance evaluation.

In multi-phase Wireless Sensor Networks (WSNs), sensor nodes are redeployed periodically to replace nodes whose batteries are depleted. In order to keep the network resilient against node capture attacks across different deployment epochs, called generations, it is necessary to refresh the key pools from which cryptographic keys are distributed. In this paper, we propose Uneven Key Pre-distribution (UKP) scheme that uses multiple different key pools at each generation. Our UKP scheme provides self healing that improves the resiliency of the network at a higher level as compared to an existing scheme in the literature. Moreover, our scheme provides perfect local and global connectivity. We conduct our simulations in mobile environment to see how our scheme performs under more realistic scenarios.

Wireless Mesh Networks (WMNs) have emerged as a promising technology to provide low cost and scalable solutions for high speed Internet access and additional services. In hybrid WMNs, where mesh clients also act as relaying agents and form a mesh client network, it is important to provide users with an efficient anonymous and accountable authentication scheme. Accountability is required for the malicious users that are to be identified and revoked from the network access and related services. Promising revocation schemes are based on Certification Revocation Lists (CRLs). Since in hybrid WMNs mesh clients also authenticate other clients, distribution of these CRLs is an important task. In this paper, we propose and examine the performance of different distribution schemes of CRLs and analyze authentication performance in two scenarios: in one scenario all mesh routers and mesh clients obtain CRLs and in the second one, CRLs are held only by the mesh routers and mesh clients acting as relaying agents require CRL checking to be performed from the router in authenticating another client.

Due to mostly being unattended, sensor nodes become open to physical attacks such as wormhole attack, which is our focus in this paper. Various solutions are proposed for wormhole attacks in sensor networks, but only a few of them take mobility of sensor nodes into account. We propose a distributed wormhole detection scheme for mobile wireless sensor networks in which mobility of sensor nodes is utilized to estimate two network features (i.e. network node density, standard deviation in network node density) through using neighboring information in a local manner. Wormhole attack is detected via observing anomalies in the neighbor nodes' behaviors based on the estimated network features and the neighboring information. We analyze the performance of proposed scheme via simulations. The results show that our scheme achieves a detection rate up to 100% with very small false positive rate (at most 1.5%) if the system parameters are chosen accordingly. Moreover, our solution requires neither" additional hardware nor tight clock synchronization which are both costly for sensor networks.

The preset distinguishing sequence generation problem is converted into a SAT problem to investigate the performance of SAT solvers for generating preset distinguishing sequences. An initial set of experiments are carried out and it is shown that the heuristics of SAT solvers can perform better than brute force algorithms that are used to generate preset distinguishing sequences.

We propose a novel scheme to provide interoperability between two of the most widely discussed identity management systems, namely CardSpace and OpenID. In this scheme, CardSpace users are able to obtain an assertion token from an OpenID-enabled identity provider, the contents of which can be processed by a CardSpace-enabled relying party. The scheme, based on a browser extension, is transparent to OpenID providers and to the CardSpace identity selector, and only requires minor changes to the operation of the CardSpace relying party.

Applications of modal logics are abundant in computer science, and a large number of structurally different modal logics have been successfully employed in a diverse spectrum of application contexts. Coalgebraic semantics, on the other hand, provides a uniform and encompassing view on the large variety of specific logics used in particular domains. The coalgebraic approach is generic and compositional: tools and techniques simultaneously apply to a large class of application areas and can, moreover, be combined in a modular way. In particular, this facilitates a pick-and-choose approach to domain-specific formalisms, applicable across the entire scope of application areas, leading to generic software tools that are easier to design, to implement and to maintain. This paper substantiates the authors’ firm belief that the systematic exploitation of the coalgebraic nature of modal logic will not only have impact on the field of modal logic itself but also lead to significant progress in a number of areas within computer science, such as knowledge representation and concurrency/mobility.

Using dependence analysis for model-based regression test suite (RTS) selection and augmentation from Extended Finite State Machine (EFSM) representations of system requirements is proposed. Given an EFSM representing the requirements of a system under test (SUT) and a set of modifications (i.e., adding, deleting, and changing transitions) on the EFSM, dependencies between transitions in the EFSM are identified. These dependencies capture the effects of the model on the modifications, the effects of the modifications on the model, and the side-effects of the modifications. The proposed method selects and augments a subset of a given test suite to form an RTS by examining dependencies covered by test cases in the given test suite.

Nuclear Magnetic Resonance (NMR1) spectroscopy involves the interaction of certain atoms which possess spin property and which are exposed to a combination of a static magnetic field and a second oscillating magnetic field. Determining the assignment of signals received from the experiments (peaks) to specific nuclei of the target molecule in NMR spectroscopy is an important challenge. Nuclear Vector Replacement (NVR) ([2, 3]) is a framework for structure-based assignments which combines multiple types of NMR data such as chemical shifts, residual dipolar couplings, and NOEs. NVR-BIP is a tool which utilizes NVR's data types as well as a scoring function with a binary integer programming (BIP) model to pair NMR peaks and amino acids for protein structure determination. The scoring function of NVR-BIP consists of 7 terms, which are added to obtain the combined score. In this paper, support vector machines (SVM) and boosting are employed to combine the terms in NVR-BIP's scoring function by viewing the assignment as a classification problem. Each peak-residue combination has a ("correct" or "incorrect") label that is predicted and the classification "score" is used as the scoring function for NVR-BIP. The assignment accuracies obtained using this approach show that boosting improves the assignment accuracy of NVR-BIP on our data set when RDCs are not available and outperforms SVMs. With RDCs, boosting and SVMs offer mixed results.

In this chapter we describe a formalism which uses the stochastic process algebra PEPA as the inscription language for labelled stochastic Petri nets. Viewed in another way, the net is used to provide a structure for linking related PEPA systems. The combined modelling language naturally represents such applications as mobile code systems where the PEPA terms are used to model the program code which moves between network hosts (the places in the net). We demonstrate the modelling capabilities of the formalism on a number of examples, including a mobile server running MobileIP.