Mohsen Guizani

Home M2M networks: Architectures, standards, and QoS improvement

It is envisioned that home networks will shift from current machine-to-human communications to the machine-to-machine paradigm with the rapid penetration of embedded devices in home surroundings. In this article, we first identify the fundamental challenges in home M2M networks. Then we present the architecture of home M2M networks decomposed into three subareas depending on the radio service ranges and potential applications. Finally, we focus on QoS management in home M2M networks, considering the increasing number of multimedia devices and growing visual requirements in a home area. Three standards for multimedia sharing and their QoS architectures are outlined. Cross-layer joint admission and rate control design is reported for QoS-aware multimedia sharing. This proposed strategy is aware of the QoS requirements and resilience of multimedia services. Illustrative results indicate that the joint design is able to intelligently allocate radio bandwidth based on QoS demands in resource-constrained home M2M networks.

DTRAB: combating against attacks on encrypted protocols through traffic-feature analysis

The unbridled growth of the Internet and the network-based applications has contributed to enormous security leaks. Even the cryptographic protocols, which are used to provide secure communication, are often targeted by diverse attacks. Intrusion detection systems (IDSs) are often employed to monitor network traffic and host activities that may lead to unauthorized accesses and attacks against vulnerable services. Most of the conventional misuse-based and anomaly-based IDSs are ineffective against attacks targeted at encrypted protocols since they heavily rely on inspecting the payload contents. To combat against attacks on encrypted protocols, we propose an anomaly-based detection system by using strategically distributed monitoring stubs (MSs). We have categorized various attacks against cryptographic protocols. The MSs, by sniffing the encrypted traffic, extract features for detecting these attacks and construct normal usage behavior profiles. Upon detecting suspicious activities due to the deviations from these normal profiles, the MSs notify the victim servers, which may then take necessary actions. In addition to detecting attacks, the MSs can also trace back the originating network of the attack. We call our unique approach DTRAB since it focuses on both Detection and TRAceBack in the MS level. The effectiveness of the proposed detection and traceback methods are verified through extensive simulations and Internet datasets.

Coalitional Game Theoretic Approach for Secondary Spectrum Access in Cooperative Cognitive Radio Networks

In this paper, we exploit a novel setting for Cognitive Radio (CR) networks to enable multiple operators to involve secondary users (SUs) as cooperative relays for their primary users. In return, SUs get an opportunity to access spare channels for their own data transmission. Initially, we assume that the CR network supports payment transfer. Then, we formulate the system as a transferable utility coalitional game. We show that there is an operating point that maximizes the sum utility over all operators and SUs while providing each player a share such that no subset of operators and SUs has an incentive to break away from the grand coalition. Such operating points exist when the solution set of the game, the core, is nonempty. Subsequently, we examine an interesting scenario where there is no payment mechanism in the network. This scenario can be investigated by using a nontransferable utility coalitional game model. We show that there exists a joint action to make the core nonempty. A general method with exponential computational complexity to get such a joint action is discussed. Then, we relate the core of this game to a competitive equilibrium of an exchange economy setting under special situations. As a result, several available efficient centralized or distributed algorithms in economics can be employed to compute a member in the core. In a nutshell, this paper constitutes the design of new coalition based dynamics that could be used in future CR networks.

Capacity of Hybrid Wireless Networks with Directional Antenna and Delay Constraint

We study the throughput capacity of hybrid wireless networks with a directional antenna. The hybrid wireless network consists of n randomly distributed nodes equipped with a directional antenna, and m regularly placed base stations connected by optical links. We investigate the ad hoc mode throughput capacity when each node is equipped with a directional antenna under an L-maximum-hop resource allocation. That is, a source node transmits to its destination only with the help of normal nodes within L hops. Otherwise, the transmission will be carried out in the infrastructure mode, i.e., with the help of base stations. We find that the throughput capacity of a hybrid wireless network greatly depends on the maximum hop L, the number of base stations m, and the beamwidth of directional antenna θ. Assuming the total bandwidth W bits/sec of the network is split into three parts, i.e., W₁ for ad hoc mode, W₂ for uplink in the infrastructure mode, and W₃ for downlink in the infrastructure mode. We show that the throughput capacity of the hybrid directional wireless network is Θ(nW₁/θ² L log n) + Θ(mW₂), if L = Ω(n^1/3/θ^4/3 log^2/3 n); and Θ((θ²L² log^2/3 n); and + Θ(mW₂), if L = o(n^1/3/θ^4/3 log^2/3 n), respectively. Finally, we analyze the impact of L, m and θ on the throughput capacity of the hybrid networks.

Bandwidth Aggregation-Aware Dynamic QoS Negotiation for Real-Time Video Streaming in Next-Generation Wireless Networks

In next generation wireless networks, Internet service providers (ISPs) are expected to offer services through several wireless technologies (e.g., WLAN, 3G, WiFi, and WiMAX). Thus, mobile computers equipped with multiple interfaces will be able to maintain simultaneous connections with different networks and increase their data communication rates by aggregating the bandwidth available at these networks. To guarantee quality-of-service (QoS) for these applications, this paper proposes a dynamic QoS negotiation scheme that allows users to dynamically negotiate the service levels required for their traffic and to reach them through one or more wireless interfaces. Such bandwidth aggregation (BAG) scheme implies transmission of data belonging to a single application via multiple paths with different characteristics, which may result in an out-of-order delivery of data packets to the receiver and introduce additional delays for packets reordering. The proposed QoS negotiation system aims to ensure the continuity of QoS perceived by mobile users while they are on the move between different access points, and also, a fair use of the network resources. The performance of the proposed dynamic QoS negotiation system is investigated and compared against other schemes. The obtained results demonstrate the outstanding performance of the proposed scheme as it enhances the scalability of the system and minimizes the reordering delay and the associated packet loss rate.

Secure and Efficient Time Synchronization in Heterogeneous Sensor Networks

Due to the collaborative nature of sensor nodes, time synchronization is critical for many sensor network operations. For sensor networks deployed in hostile environments, security is critical to the success of time synchronization. Over the past few years, a number of secure time-synchronization schemes have been proposed for sensor networks. However, these schemes are designed for homogeneous sensor networks and may incur large communication/computation overhead or may cause accumulated synchronization errors (due to multihop relays of time reference messages). Several works have shown that better performance and security can be achieved in heterogeneous sensor networks (HSNs). In this paper, we present a secure and efficient time synchronization scheme for HSNs by utilizing powerful high-end sensors. We implement the time-synchronization scheme in real sensor nodes, and the experiments show that our scheme achieves higher synchronization accuracy than a popular sensor time-synchronization scheme. The analyses demonstrate that our scheme is resilient to various attacks and significantly reduces communication overhead.

Network Modeling and Simulation: A Practical Perspective

Preface. Acknowledgements. 1 Basic Concepts and Techniques. 1.1 Why Is Simulation Important? 1.2 What Is a Model? 1.3 Performance Evaluation Techniques. 1.4 Development of Systems Simulation. 1.5 Summary. Recommended Reading. 2 Designing and Implementing a Discrete-Event Simulation Framework. 2.1 The Scheduler. 2.2 The Simulation Entities. 2.3 The Events. 2.4 Tutorial 1: Hello World. 2.5 Tutorial 2: Two-Node Hello Protocol. 2.6 Tutorial 3: Two-Node Hello through a Link. 2.7 Tutorial 4: Two-Node Hello through a Lossy Link. 2.8 Summary. Recommended Reading. 3 Honeypot Communities: A Case Study with the Discrete-Event Simulation Framework. 3.1 Background. 3.2 System Architecture. 3.3 Simulation Modeling. 3.4 Simulation Execution. 3.5 Output Analysis. 3.6 Summary. Recommended Reading. 4 Monte Carlo Simulation. 4.1 Characteristics of Monte Carlo Simulations. 4.2 The Monte Carlo Algorithm. 4.3 Merits and Drawbacks. 4.4 Monte Carlo Simulation for the Electric Car Charging Station. 4.5 Summary. Recommended Reading. 5 Network Modeling. 5.1 Simulation of Networks. 5.2 The Network Modeling and Simulation Process. 5.3 Developing Models. 5.4 Network Simulation Packages. 5.5 OPNET: A Network Simulation Package. 5.6 Summary. Recommended Reading. 6 Designing and Implementing CASiNO: A Network Simulation Framework. 6.1 Overview. 6.2 Conduits. 6.3 Visitors. 6.4 The Conduit Repository. 6.5 Behaviors and Actors. 6.6 Tutorial 1: Terminals. 6.7 Tutorial 2: States. 6.8 Tutorial 3: Making Visitors. 6.9 Tutorial 4: Muxes. 6.10 Tutorial 5: Factories. 6.11 Summary. Recommended Reading. 7 Statistical Distributions and Random Number Generation. 7.1 Introduction to Statistical Distributions. 7.2 Discrete Distributions. 7.3 Continuous Distributions. 7.4 Augmenting CASiNO with Random Variate Generators. 7.5 Random Number Generation. 7.6 Frequency and Correlation Tests. 7.7 Random Variate Generation. 7.8 Summary. Recommended Reading. 8 Network Simulation Elements: A Case Study Using CASiNO. 8.1 Making a Poisson Source of Packets. 8.2 Making a Protocol for Packet Processing. 8.3 Bounding Protocol Resources. 8.4 Making a Round-Robin (De)multiplexer. 8.5 Dynamically Instantiating Protocols. 8.6 Putting It All Together. 8.7 Summary. 9 Queuing Theory. 9.1 Introduction to Stochastic Processes. 9.2 Discrete-Time Markov Chains. 9.3 Continuous-Time Markov Chains. 9.4 Basic Properties of Markov Chains. 9.5 Chapman-Kolmogorov Equation. 9.6 Birth-Death Process. 9.7 Littles Theorem. 9.8 Delay on a Link. 9.9 Standard Queuing Notation. 9.10 The M/M/ 1 Queue. 9.11 The M/M/m Queue. 9.12 The M/M/ 1 /b Queue. 9.13 The M/M/m/m Queue. 9.14 Summary. Recommended Reading. 10 Input Modeling and Output Analysis. 10.1 Data Collection. 10.2 Identifying the Distribution. 10.3 Estimation of Parameters for Univariate Distributions. 10.4 Goodness-of-Fit Tests. 10.5 Multivariate Distributions. 10.6 Selecting Distributions without Data. 10.7 Output Analysis. 10.8 Summary. Recommended Reading. 11 Modeling Network Traffic. 11.1 Introduction. 11.2 Network Traffic Models. 11.3 Traffic Models for Mobile Networks. 11.4 Global Optimization Techniques. 11.5 Particle Swarm Optimization. 11.6 Optimization in Mathematics. 11.7 Summary. Recommended Reading. Index.

LMAT: Localization with a Mobile Anchor Node Based on Trilateration in Wireless Sensor Networks

Currently, in Wireless Sensor Networks (WSNs), the main idea in most localization algorithms has been that a mobile anchor node, e.g., GPS-equipped (Global Positioning System) nodes, broadcasts its coordinates to locate unknown nodes. In this case, a basic problem is the path planning of the mobile anchor node which should move along the trajectory to minimize the localization error and locate the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT). LMAT algorithm uses a mobile anchor node to move according to the equilateral triangle trajectory in deployment area. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms, e.g., SPIRAL, SCAN, DOUBLE SCAN and HILBERT algorithms.

A novel IEEE 802.11-based MAC protocol supporting cooperative communications

Cooperative diversity is a transmission technique, where multiple terminals form a virtual antenna array that realizes spatial diversity gain in a distributed fashion. The concept of cooperation has already been introduced to MAC layer to design MAC protocol. But it does not take advantage of physical layers cooperation. In this paper, we present a novel MAC protocol based on IEEE 802.11, called C-MAC, which is able to support the basic building block of cooperative system. In other words, in C-MAC, a source would invite a relay node into data transmission if there exits an available one. During data transmission, the source sends the signal to destination in the first time slot. The relay node will retransmit the overheard information to the destination in the second time slot. The destination combines two signals from the source and the helper to create the spatial diversity and robustness against channel fading. The C-MAC is backward compatible to the legacy IEEE 802.11 system. The performance of C-MAC mainly depends on physical layers performance as it just provides the support for cooperation at the MAC layer. If the physical layer works well, C-MAC would outperform IEEE 802.11 when considering packet error rate (PER). We also perform extensive simulation using ns-2 with assumptive physical parameters. The results show that C-MAC would outperform 802.11 if PER is over some threshold, e.g. when PER is 0.4, C-MAC can achieve up to 11.5% higher throughput than IEEE 802.11. Copyright

A Two-Step Secure Localization for Wireless Sensor Networks

Accuratelylocatingunknownnodesisacriticalissueinthestudyofwirelesssensornetworks(WSNs). Many localization approaches have been proposed based on anchor nodes, which are assumed to know their locations by manual placement or additional equipments such as global positioning system. However, none of these approaches can work properly under the adversarial scenario. In this paper, we propose a novel scheme called two-step secure localization (TSSL) stand against many typical malicious attacks, e.g. wormhole attack and location spoofing attack. TSSL detects malicious nodes step by step. First, anchor nodes collaborate with each other to identify suspicious nodes by checkingtheircoordinates,identitiesandtimeofsendinginformation.Then,byusingamodifiedmesh generationscheme,maliciousnodesareisolatedandtheWSNisdividedintoareaswithdifferenttrust grades. Finally, a novel localization algorithm based on the arrival time difference of localization information is adopted to calculate locations of unknown nodes. Simulation results show that the TSSL detects malicious nodes effectively and the localization algorithm accomplishes localization with high localization accuracy.