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Multi-agent systems (MAS) have been of interest to many researchers during the last decades. This thesis focuses on multi-robot systems (MRS) and programmable matter as two types of MAS. Regarding MRS, the focus is on the 'mergeable nervous system' (MNS) concept which allows the robots to connect to one another and establish a communication network through self-organization and then use the network to temporarily report sensing events and cede authority to a single robot in the system. Here, in a collective perception scenario, we experimentally evaluate the performance of an MNS enabled approach and compare it with that of several decentralized benchmark approaches. We show that an MNS-enabled approach is high-performing, fault-tolerant, and scalable, so it is an appropriate approach for MRS. As a goal of the thesis, using an MNS-enabled approach, we present for the first time a comprehensive comparison of control architectures in multi-robot systems, which includes a comparison of accuracy, efficiency, speed, energy consumption, scalability, and fault tolerance. Our comparisons provide designers of multi-robot systems with a better understanding for selecting the best-performing control depending on the system’s objectives. Additionally, as a separate goal, we design a high-level leader-based programmable matter, which can perform some basic primitive operations in a grid environment, and construct it using lower-level organisms. We design and implement deterministic algorithms for "curl" operation of this high-level matter, an instance of shape formation problem. We prove the correctness of the presented algorithms, analytically determine their complexity, and experimentally evaluate their performance., Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

Many real world applications modelled as complex networks optimize multiple conflicting objective functions that change frequently over time. Solving multi-objective optimization problems (MOP) on complex networks, either static or dynamic, is NP-hard. Evolutionary multi-objective optimization (EMO) heuristics based on Pareto criterion (PC) dominance and non-Pareto criterion (NPC) decomposition techniques have been successfully used to solve dynamic MOP (DMOP). However, addressing both convergence and diversity, simultaneously, in either PC or NPC evolution is a challenge. Moreover, existing metrics are not robust to evaluate EMO algorithms for solving varying real-world applications that exhibit dynamic behaviour. The contribution of this thesis is therefore two-fold: (i) we propose a co-evolutionary bi-criterion algorithm: Cooperative, Concurrent, Coevolutionary Multi-Objective (Co3_MO), that exploits the strengths of PC and NPC evolution to solve DMOP; (ii) we propose a new evaluation framework and metrics to measure convergence, diversity and dynamism of dynamic EMO algorithms. We study our proposed algorithm, framework and metrics on the multi-objective travelling salesperson problem (MTSP) incorporating two popular complementary evolutionary techniques (NSGA-II and MOEA/D). We perform various experiments on both static and dynamic MTSP datasets and show that Co3_MO with local search and hybrid operators performs better than stand-alone EMO techniques. We also show that our proposed framework and metrics are flexible to incorporate domain-specific knowledge of an application to capture convergence, diversity and dynamism of the evaluated EMO algorithms.

Multi-agent systems (MAS) have been of interest to many researchers during the last decades. This thesis focuses on multi-robot systems (MRS) and programmable matter as two types of MAS. Regarding MRS, the focus is on the ’mergeable nervous systems’ (MNS) concept which allows the robots to connect to one another and establish a communication network through self-organization and then use the network to temporarily report sensing events and cede authority to a single robot in the system. Here, in a collective perception scenario, we experimentally evaluate the performance of an MNS-enabled approach and compare it with that of several decentralized benchmark approaches. We show that an MNS enabled approach is high-performing, fault-tolerant, and scalable, so it is an appropriate approach for MRS. As a goal of the thesis, using an MNS-enabled approach, we present for the first time a comprehensive comparison of control architectures in multi-robot systems, which includes a comparison of accuracy, efficiency, speed, energy consumption, scalability, and fault tolerance. Our comparisons provide designers of multi robot systems with a better understanding for selecting the best-performing control depending on the system’s objectives. Additionally, as a separate goal, we design a high-level leader-based programmable matter, which can perform some basic primitive operations in a grid environment, and construct it using lower-level organisms. We design and implement deterministic algorithms for “curl” operation of this high-level matter, an instance of shape formation problem. We prove the correctness of the presented algorithms, analytically determine their complexity, and experimentally evaluate their performance., Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

Many real world applications modelled as complex networks optimize multiple conflicting objective functions that change frequently over time. Solving multi-objective optimization problems (MOP) on complex networks, either static or dynamic, is NP-hard. Evolutionary multi-objective optimization (EMO) heuristics based on Pareto criterion (PC) dominance and non-Pareto criterion (NPC) decomposition techniques have been successfully used to solve dynamic MOP (DMOP). However, addressing both convergence and diversity, simultaneously, in either PC or NPC evolution is a challenge. Moreover, existing metrics are not robust to evaluate EMO algorithms for solving varying real-world applications that exhibit dynamic behaviour. The contribution of this thesis is therefore two-fold: (i) we propose a co-evolutionary bi-criterion algorithm: Cooperative, Concurrent, Coevolutionary Multi-Objective (Co3_MO), that exploits the strengths of PC and NPC evolution to solve DMOP; (ii) we propose a new evaluation framework and metrics to measure convergence, diversity and dynamism of dynamic EMO algorithms. We study our proposed algorithm, framework and metrics on the multi-objective travelling salesperson problem (MTSP) incorporating two popular complementary evolutionary techniques (NSGA-II and MOEA/D). We perform various experiments on both static and dynamic MTSP datasets and show that Co3_MO with local search and hybrid operators performs better than stand-alone EMO techniques. We also show that our proposed framework and metrics are flexible to incorporate domain-specific knowledge of an application to capture convergence, diversity and dynamism of the evaluated EMO algorithms.

The attention mechanism has been widely used in deep neural networks as a model component. By now, it has become a critical building block in many state-of-the-art natural language models. Despite its great success established empirically, the working mechanism of attention has not been investigated at a sufficient theoretical depth to date. In this paper, we set up a simple text classification task and study the dynamics of training a simple attention-based classification model using gradient descent. In this setting, we show that, for the discriminative words that the model should attend to, a persisting identity exists relating its embedding and the inner product of its key and the query. This allows us to prove that training must converge to attending to the discriminative words when the attention output is classified by a linear classifier. Experiments are performed, which validate our theoretical analysis and provide further insights.

Teams of multiple mobile robots may communicate with each-other using a wireless ad-hoc network. Fault-tolerance in communication can be achieved by making the communication network bi-connected. We present the first localized protocol for constructing a fault-tolerant bi-connected robotic network topology from a connected network, in such a way that the total movement of robots is minimized. The proposed distributed algorithm uses p-hop neighbor information to identify critical head robots that can direct two neighbors to move toward each other and bi-connect their neighborhood. Simulation results show that the total distance of movement of robots decreases significantly (e.g. about 2.5 times for networks with density 10) with our localized algorithm when compared to the existing globalized one. Proposed localized algorithm does not guarantee bi-connectivity, may partition the network, and may even stop at connected but not bi-connected stage. However, our algorithm achieved 100% success on all networks with average degrees ≥10, and over 70% success on sparse networks with average degrees ≥5

Full quantum capability devices can provide secure communications, but they are challenging to make portable given the current technology. Besides, classical portable devices are unable to construct communication channels resistant to quantum computers. Hence, communication security on portable devices cannot be guaranteed. Semi-Quantum Communication (SQC) attempts to break the quandary by lowering the receiver's required quantum capability so that secure communications can be implemented on a portable device. However, all SQC protocols have low qubit efficiency and complex hardware implementations. The protocols involving quantum entanglement require linear Entanglement Preservation Time (EPT) and linear quregister size. In this paper, we propose two new keyless SQC protocols that address the aforementioned weaknesses. They are named Economic Keyless Semi-Quantum Point-to-point Communication (EKSQPC) and Rate Estimation EKSQPC (REKSQPC). They achieve theoretically constant minimal EPT and quregister size, regardless of message length. We show that the new protocols, with low overhead, can detect Measure and Replay Attacks (MRAs). REKSQDC is tolerant to transmission impairments and environmental perturbations. The protocols are based on a new quantum message transmission operation termed Tele-Fetch. Like QKD, their strength depends on physical principles rather than mathematical complexity.

Network structures, consisting of nodes and edges, have applications in almost all subjects. A set of nodes is called a community if the nodes have strong interrelations. Industries (including cell phone carriers and online social media companies) need community structures to allocate network resources and provide proper and accurate services. However, all the current detection algorithms are motivated by the practical problems, whose applicabilities in other fields are open to question. Thence, for a new community problem, researchers need to derive algorithms ad hoc, which is arduous and even unnecessary. In this paper, we represent a general procedure to find community structures in practice. We mainly focus on two typical types of networks: transmission networks and similarity networks. We reduce them to a unified graph model, based on which we propose a general method to define and detect communities. Readers can specialize our general algorithm to accommodate their problems. In the end, we also give a demonstration to show how the algorithm works.

Sensor networks are widely used in various domains like the intelligent transportation systems. Users issue queries to sensors and collect sensing data. Due to the low quality sensing devices or random link failures, sensor data are often noisy. In order to increase the reliability of the query results, continuous queries are often employed. In this work we focus on continuous holistic queries like Median. Existing approaches are mainly designed for non-holistic queries like Average. However, it is not trivial to answer holistic ones due to their non-decomposable property. We first propose two schemes based on the data correlation between different rounds, with one for getting the exact answers and the other one for deriving the approximate results. We then combine the two proposed schemes into a hybrid approach, which is adaptive to the data changing speed. We evaluate this design through extensive simulations. The results show that our approach significantly reduces the traffic cost compared with previous works while maintaining the same accuracy. © 1990-2012 IEEE.

Many algorithms have been introduced to deterministically authenticate Radio Frequency Identification (RFID) tags, while little work has been done to address scalability issue in batch authentications. Deterministic approaches verify tags one by one, and the communication overhead and time cost grow linearly with increasing size of tags. We design a fast and scalable counterfeits estimation scheme, INformative Counting (INC), which achieves sublinear authentication time and communication cost in batch verifications. The key novelty of INC builds on an FM-Sketch variant authentication synopsis that can capture key counting information using only sublinear space. With the help of this well-designed data structure, INC is able to provide authentication results with accurate estimates of the number of counterfeiting tags and genuine tags, while previous batch authentication methods merely provide 0/1 results indicating the existence of counterfeits. We conduct detailed theoretical analysis and extensive experiments to examine this design and the results show that INC significantly outperforms previous work in terms of effectiveness and efficiency.

Many algorithms have been introduced to deterministically authenticate Radio Frequency Identification (RFID) tags, while little work has been done to address scalability issue in batch authentications. Deterministic approaches verify tags one by one, and the communication overhead and time cost grow linearly with increasing size of tags. We design a fast and scalable counterfeits estimation scheme, INformative Counting (INC), which achieves sublinear authentication time and communication cost in batch verifications. The key novelty of INC builds on an FM-Sketch variant authentication synopsis that can capture key counting information using only sublinear space. With the help of this well-designed data structure, INC is able to provide authentication results with accurate estimates of the number of counterfeiting tags and genuine tags, while previous batch authentication methods merely provide 0/1 results indicating the existence of counterfeits. We conduct detailed theoretical analysis and extensive experiments to examine this design and the results show that INC significantly outperforms previous work in terms of effectiveness and efficiency.

Sensor networks are widely used in various domains like the intelligent transportation systems. Users issue queries to sensors and collect sensing data. Due to the low quality sensing devices or random link failures, sensor data are often noisy. In order to increase the reliability of the query results, continuous queries are often employed. In this work we focus on continuous holistic queries like Median. Existing approaches are mainly designed for non-holistic queries like Average. However, it is not trivial to answer holistic ones due to their non-decomposable property. We first propose two schemes based on the data correlation between different rounds, with one for getting the exact answers and the other one for deriving the approximate results. We then combine the two proposed schemes into a hybrid approach, which is adaptive to the data changing speed. We evaluate this design through extensive simulations. The results show that our approach significantly reduces the traffic cost compared with previous works while maintaining the same accuracy. © 1990-2012 IEEE.

Many real-world applications use random numbers generated by pseudo-random number and true random number generators (TRNG). Unlike pseudo-random number generators which rely on an input seed to generate random numbers, a TRNG relies on a non-deterministic source to generate aperiodic random numbers. In this research, we develop a novel and generic software-based TRNG using a random source extracted from compute architectures of today. We show that the non-deterministic events such as race conditions between compute threads follow a near Gamma distribution, independent of the architecture, multi-cores or co-processors. Our design improves the distribution towards a uniform distribution ensuring the stationarity of the sequence of random variables. We improve the random numbers statistical deficiencies by using a post-processing stage based on a heuristic evolutionary algorithm. Our post-processing algorithm is composed of two phases: (i) Histogram Specification and (ii) Stationarity Enforcement. We propose two techniques for histogram equalization, Exact Histogram Equalization (EHE) and Adaptive EHE (AEHE) that maps the random numbers distribution to a user-specified distribution. EHE is an offline algorithm with O(NlogN). AEHE is an online algorithm that improves performance using a sliding window and achieves O(N). Both algorithms ensure a normalized entropy of (0:95; 1:0]. The stationarity enforcement phase uses genetic algorithms to mitigate the statistical deficiencies from the output of histogram equalization by permuting the random numbers until wide-sense stationarity is achieved. By measuring the power spectral density standard deviation, we ensure that the quality of the numbers generated from the genetic algorithms are within the specified level of error defined by the user. We develop two algorithms, a naive algorithm with an expected exponential complexity of E[O(eN)], and an accelerated FFT-based algorithm with an expected quadratic complexity of E[O(N2

Many algorithms have been introduced to deterministically authenticate Radio Frequency Identification (RFID) tags, while little work has been done to address scalability issue in batch authentications. Deterministic approaches verify tags one by one, and the communication overhead and time cost grow linearly with increasing size of tags. We design a fast and scalable counterfeits estimation scheme, INformative Counting (INC), which achieves sublinear authentication time and communication cost in batch verifications. The key novelty of INC builds on an FM-Sketch variant authentication synopsis that can capture key counting information using only sublinear space. With the help of this well-designed data structure, INC is able to provide authentication results with accurate estimates of the number of counterfeiting tags and genuine tags, while previous batch authentication methods merely provide 0/1 results indicating the existence of counterfeits. We conduct detailed theoretical analysis and extensive experiments to examine this design and the results show that INC significantly outperforms previous work in terms of effectiveness and efficiency.

Sensor networks are widely used in various domains like the intelligent transportation systems. Users issue queries to sensors and collect sensing data. Due to the low quality sensing devices or random link failures, sensor data are often noisy. In order to increase the reliability of the query results, continuous queries are often employed. In this work we focus on continuous holistic queries like Median. Existing approaches are mainly designed for non-holistic queries like Average. However, it is not trivial to answer holistic ones due to their non-decomposable property. We first propose two schemes based on the data correlation between different rounds, with one for getting the exact answers and the other one for deriving the approximate results. We then combine the two proposed schemes into a hybrid approach, which is adaptive to the data changing speed. We evaluate this design through extensive simulations. The results show that our approach significantly reduces the traffic cost compared with previous works while maintaining the same accuracy.

Many real-world applications use random numbers generated by pseudo-random number and true random number generators (TRNG). Unlike pseudo-random number generators which rely on an input seed to generate random numbers, a TRNG relies on a non-deterministic source to generate aperiodic random numbers. In this research, we develop a novel and generic software-based TRNG using a random source extracted from compute architectures of today. We show that the non-deterministic events such as race conditions between compute threads follow a near Gamma distribution, independent of the architecture, multi-cores or co-processors. Our design improves the distribution towards a uniform distribution ensuring the stationarity of the sequence of random variables. We improve the random numbers statistical deficiencies by using a post-processing stage based on a heuristic evolutionary algorithm. Our post-processing algorithm is composed of two phases: (i) Histogram Specification and (ii) Stationarity Enforcement. We propose two techniques for histogram equalization, Exact Histogram Equalization (EHE) and Adaptive EHE (AEHE) that maps the random numbers distribution to a user-specified distribution. EHE is an offline algorithm with O(NlogN). AEHE is an online algorithm that improves performance using a sliding window and achieves O(N). Both algorithms ensure a normalized entropy of (0:95; 1:0]. The stationarity enforcement phase uses genetic algorithms to mitigate the statistical deficiencies from the output of histogram equalization by permuting the random numbers until wide-sense stationarity is achieved. By measuring the power spectral density standard deviation, we ensure that the quality of the numbers generated from the genetic algorithms are within the specified level of error defined by the user. We develop two algorithms, a naive algorithm with an expected exponential complexity of E[O(eN)], and an accelerated FFT-based algorithm with an expected quadratic complexity of E[O(N2

Many TDMA- and CSMA-based protocols try to obtain fair channel access and to increase channel utilization. It is still challenging and crucial in Wireless Sensor Networks (WSNs), especially when the time synchronization cannot be well guaranteed and consumes much extra energy. This paper presents a localized and ondemand scheme ADC to adaptively adjust duty cycle based on quorum systems. ADC takes advantages of TDMA and CSMA and guarantees that (1) each node can fairly access channel based on its demand, (2) channel utilization can be increased by reducing competition for channel access among neighboring nodes, (3) every node has at least one rendezvous active time slot with each of its neighboring nodes even under asynchronization. The latency bound of data aggregation is analyzed under ADC to show that ADC can bound the latency under both synchronization and asynchronization. We conduct extensive experiments in TinyOS on a real test-bed with TelosB nodes to evaluate the performance of ADC. Comparing with B-MAC, ADC substantially reduces the contention for channel access and energy consumption, and improves network throughput., Comment: 11 pages, 13 figures, IEEE 8th International Conference on Mobile Ad hoc and Sensor Systems (MASS, 2011)

Software-defined network (SDN) is the next generation of networking architecture that is dynamic, manageable, cost-effective, and adaptable, making it ideal for the high-bandwidth, dynamic nature of today's applications. In SDN, network management is facilitated through software rather than low-level device configurations. However, the centralized control plane introduced by SDN imposes a great challenge for the network security. In this paper, we present a secure SDN structure, in which each device is managed by multiple controllers, not just a single as in a traditional manner, with the dynamic and isolated instance provided by the cloud. It can resist Byzantine attacks on controllers and the communication links between controllers and SDN switches. Furthermore, we study a controller minimization problem with security requirement and propose a cost-efficient controller assignment algorithm with a constant approximation ratio. From the experiment result, the secure SDN structure has little impact on the network latency, provide better security than general distributed controller, and the proposed algorithm performs higher efficiency than random assignment. © 2014 IEEE.

Cloud computing is a general term for system architectures that involves delivering hosted services over the Internet, made possible by significant innovations in virtualization and distributed computing, as well as improved access to high-speed Internet. A cloud service differs from traditional hosting in three principal aspects. First, it is provided on demand, typically by the minute or the hour; second, it is elastic since the user can have as much or as little of a service as they want at any given time; and third, the service is fully managed by the provider -- user needs little more than computer and Internet access. Typically a contract is negotiated and agreed between a customer and a service provider; the service provider is required to execute service requests from a customer within negotiated quality of service (QoS) requirements for a given price. Due to dynamic nature of cloud environments, diversity of user's requests, resource virtualization, and time dependency of load, provides expected quality of service while avoiding over-provisioning is not a simple task. To this end, cloud provider must have efficient and accurate techniques for performance evaluation of cloud computing centers. The development of such techniques is the focus of this thesis. This thesis has two parts. In first part, Chapters 2, 3 and 4, monolithic performance models are developed for cloud computing performance analysis. We begin with Poisson task arrivals, generally distributed service times, and a large number of physical servers. Later on, we extend our model to include finite buffer capacity, batch task arrivals, and virtualized servers with a large number of virtual machines in each physical machine. However, a monolithic model may suffer from intractability and poor scalability due to large number of parameters. Therefore, in the second part of the thesis (Chapters 5 and 6) we develop and evaluate tractable functional performance sub-models for different servicing steps

Cloud computing is a general term for system architectures that involves delivering hosted services over the Internet, made possible by significant innovations in virtualization and distributed computing, as well as improved access to high-speed Internet. A cloud service differs from traditional hosting in three principal aspects. First, it is provided on demand, typically by the minute or the hour; second, it is elastic since the user can have as much or as little of a service as they want at any given time; and third, the service is fully managed by the provider -- user needs little more than computer and Internet access. Typically a contract is negotiated and agreed between a customer and a service provider; the service provider is required to execute service requests from a customer within negotiated quality of service (QoS) requirements for a given price. Due to dynamic nature of cloud environments, diversity of user's requests, resource virtualization, and time dependency of load, provides expected quality of service while avoiding over-provisioning is not a simple task. To this end, cloud provider must have efficient and accurate techniques for performance evaluation of cloud computing centers. The development of such techniques is the focus of this thesis. This thesis has two parts. In first part, Chapters 2, 3 and 4, monolithic performance models are developed for cloud computing performance analysis. We begin with Poisson task arrivals, generally distributed service times, and a large number of physical servers. Later on, we extend our model to include finite buffer capacity, batch task arrivals, and virtualized servers with a large number of virtual machines in each physical machine. However, a monolithic model may suffer from intractability and poor scalability due to large number of parameters. Therefore, in the second part of the thesis (Chapters 5 and 6) we develop and evaluate tractable functional performance sub-models for different servicing steps

Sensor networks aim at monitoring their surroundings for event detection and object tracking. But due to failure or death of sensors, false signal can be transmitted. In this paper, we consider the problem of fault detection in wireless sensor network (WSN), in particular, addressing both the noise-related measurement error and sensor fault simultaneously in fault detection. We assume that the sensors are placed at the center of a square (or hexagonal) cell in region of interest (ROI) and, if the event occurs, it occurs at a particular cell of the ROI. We propose fault detection schemes that take into account error probabilities into the optimal event detection process. We develop the schemes under the consideration of Neyman-Pearson test and Bayes test.

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes., Comment: 12 pages

We propose an integrated architecture for smart grids, that supports data aggregation and access control. Data can be aggregated by home area network, building area network and neighboring area network in such a way that the privacy of customers is protected. We use homomorphic encryption technique to achieve this. The consumer data that is collected is sent to the substations where it is monitored by remote terminal units (RTU). The proposed access control mechanism gives selective access to consumer data stored in data repositories and used by different smart grid users. Users can be maintenance units, utility centers, pricing estimator units or analyzing and prediction groups. We solve this problem of access control using cryptographic technique of attribute-based encryption. RTUs and users have attributes and cryptographic keys distributed by several key distribution centers (KDC). RTUs send data encrypted under a set of attributes. Users can decrypt information provided they have valid attributes. The access control scheme is distributed in nature and does not rely on a single KDC to distribute keys. Bobba \emph{et al.} \cite{BKAA09} proposed an access control scheme, which relies on a centralized KDC and is thus prone to single-point failure. The other requirement is that the KDC has to be online, during data transfer which is not required in our scheme. Our access control scheme is collusion resistant, meaning that users cannot collude and gain access to data, when they are not authorized to access. We theoretically analyze our schemes and show that the computation overheads are low enough to be carried out in smart grids. To the best of our knowledge, ours is the first work on smart grids, which integrates these two important security components (privacy preserving data aggregation and access control) and presents an overall security architecture in smart grids., Comment: 12 Pages, 3 figures

Teams of multiple mobile robots may communicate with each-other using a wireless ad-hoc network. Fault-tolerance in communication can be achieved by making the communication network bi-connected. We present the first localized protocol for constructing a fault-tolerant bi-connected robotic network topology from a connected network, in such a way that the total movement of robots is minimized. The proposed distributed algorithm uses p-hop neighbor information to identify critical head robots that can direct two neighbors to move toward each other and bi-connect their neighborhood. Simulation results show that the total distance of movement of robots decreases significantly (e.g. about 2.5 times for networks with density 10) with our localized algorithm when compared to the existing globalized one. Proposed localized algorithm does not guarantee bi-connectivity, may partition the network, and may even stop at connected but not bi-connected stage. However, our algorithm achieved 100% success on all networks with average degrees ≥10, and over 70% success on sparse networks with average degrees ≥5