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DATA COLLECTION PROTOCOL- A QoS-Aware Data Collection Protocol For LLNs In Fog-Enabled Internet Of Things

Improving Quality Of Service (QoS) Of Low Power And Lossy Networks (LLNs) In Internet Of Things (IoT) Is A Major Challenge. Cluster-based Routing Technique Is An Effective Approach To Achieve This Goal. This Paper Proposes A QoS-aware Clustering-based Routing (QACR) Mechanism For LLNs In Fog-enabled IoT Which Provides A Clustering, A Cluster Head (CH) Election, And A Routing Path Selection Technique. The Clustering Adopts The Community Detection Algorithm That Partitions The Network Into Clusters With Available Nodes' Connectivity. The CH Election And Relay Node Selection Both Are Weighted By The Rank Of The Nodes Which Take Node's Energy, Received Signal Strength, Link Quality, And Number Of Cluster Members Into Consideration As The Ranking Metrics. The Number Of CHs In A Cluster Is Adaptive And Varied According To A Cluster State To Balance The Energy Consumption Of Nodes. Besides, The Protocol Uses The CH Role Handover Technique During CH Election That Decreases The Control Messages For The Periodic Election And Cluster Formation In Detail. An Evaluation Of The QACR Has Performed Through Simulations For Various Scenarios. The Obtained Results Show That The QACR Improves The QoS In Terms Of Packet Delivery Ratio, Latency, And Network Lifetime Compared To The Existing Protocols.

LIGHTWEIGHT GROUP KEY MANAGEMENT- Decentralized Lightweight Group Key Management For Dynamic Access Control In IoT Environments

Rapid Growth Of Internet Of Things (IoT) Devices Dealing With Sensitive Data Has Led To The Emergence Of New Access Control Technologies In Order To Maintain This Data Safe From Unauthorized Use. In Particular, A Dynamic IoT Environment, Characterized By A High Signaling Overhead Caused By Subscribers' Mobility, Presents A Significant Concern To Ensure Secure Data Distribution To Legitimate Subscribers. Hence, For Such Dynamic Environments, Group Key Management (GKM) Represents The Fundamental Mechanism For Managing The Dissemination Of Keys For Access Control And Secure Data Distribution. However, Existing Access Control Schemes Based On GKM And Dedicated To IoT Are Mainly Based On Centralized Models, Which Fail To Address The Scalability Challenge Introduced By The Massive Scale Of IoT Devices And The Increased Number Of Subscribers. Besides, None Of The Existing GKM Schemes Supports The Independence Of The Members In The Same Group. They Focus Only On Dependent Symmetric Group Keys Per Subgroup Communication, Which Is Inefficient For Subscribers With A Highly Dynamic Behavior. To Deal With These Challenges, We Introduce A Novel Decentralized Lightweight Group Key Management Architecture For Access Control In The IoT Environment (DLGKM-AC). Based On A Hierarchical Architecture, Composed Of One Key Distribution Center (KDC) And Several Sub Key Distribution Centers (SKDCs), The Proposed Scheme Enhances The Management Of Subscribers' Groups And Alleviate The Rekeying Overhead On The KDC. Moreover, A New Master Token Management Protocol For Managing Keys Dissemination Across A Group Of Subscribers Is Introduced. This Protocol Reduces Storage, Computation, And Communication Overheads During Join/leave Events. The Proposed Approach Accommodates A Scalable IoT Architecture, Which Mitigates The Single Point Of Failure By Reducing The Load Caused By Rekeying At The Core Network. DLGKM-AC Guarantees Secure Group Communication By Preventing Collusion Attacks And Ensuring Backward/forward Secrecy. Simulation Results And Analysis Of The Proposed Scheme Show Considerable Resource Gain In Terms Of Storage, Computation, And Communication Overheads.

SOFTWAER DEFINED NETWORKS -QoC-Aware Control Traffic Engineering In Software Defined Networks

In A Distributed Software Defined Networking (SDN) Architecture, The Quality Of Service (QoS) Experienced By A Traffic Flow Through An SDN Switch Is Primarily Dependant On The SDN Controller To Which That Switch Is Mapped. We Propose A New Controller-quality Metric Known As The Quality Of Controller (QoC) Which Is Defined Based On The Controller’s Reliability And Response Time. We Model The Controller Reliability Based On Bayesian Inference While Its Response Time Is Modelled As A Linear Approximation Of The M/M/1 Queue. We Develop A QoC-aware Approach For Solving (i) The Switch-controller Mapping Problem And, (ii) Control Traffic Distribution Among The Mapped Controllers. Each Switch Is Mapped To Multiple Controllers To Enable Resilience With The Switch-controller Mapping And Control Traffic Distribution Based On The QoC Metric Which Is The Combined Cost Of Controller Reliability And Response Time. We First Develop An Optimization Programming Formulation That Maximizes The Minimum QoC Among The Set Of Controllers To Solve The Above Problem. Since The Optimization Problem Is Computationally Prohibitive For Large Networks, We Develop A Heuristic Algorithm — Qoc-Aware Switch-coNTroller Mapping (QuANTuM) — That Solves The Problem Of Switch-controller Mapping And Control Traffic Distribution In Two Stages Such That The Minimum Of The Controller QoC Is Maximized. Through Simulations, We Show That The Heuristic Results Are Within 18% Of The Optimum While Achieving A Fair Control Traffic Distribution With A QoC Min-max Ratio Of Up To 95%.

RUMOR MODEL- Rumor Diffusion Model Based On Representation Learning And Anti-Rumor

The Traditional Rumor Diffusion Model Primarily Studies The Rumor Itself And User Behavior As The Entry Points. The Complexity Of User Behavior, Multidimensionality Of The Communication Space, Imbalance Of The Data Samples, And Symbiosis And Competition Between Rumor And Anti-rumor Are Challenges Associated With The In-depth Study On Rumor Communication. Given These Challenges, This Study Proposes A Group Behavior Model For Rumor And Anti-rumor. First, This Study Considers The Diversity And Complexity Of The Rumor Propagation Feature Space And The Advantages Of Representation Learning In The Feature Extraction Of Data. Further, We Adopt The Corresponding Representation Learning Methods For Their Content And Structure Of The Rumor And Anti-rumor To Reduce The Spatial Feature Dimension Of The Rumor-spreading Data And To Uniformly And Densely Express The Full-featured Information Feature Representation. Second, This Paper Introduces An Evolutionary Game Theory, Which Is Combined With The User-influenced Rumor And Anti-rumor, To Reflect The Conflict And Symbiotic Relationship Between Rumor And Anti-rumor. We Obtain A Network Structural Feature Expression Of The Influence Degree Of Users On Rumor And Anti-rumor When Expressing The Structural Characteristics Of Group Communication Relationships. Finally, Aiming At The Timeliness Of Rumor Topic Evolution, The Whole Model Is Proposed. Time Slice And Discretize The Life Cycle Of Rumor Is Used To Synthesize The Full-featured Information Feature Representation Of Rumor And Anti-rumor. The Experiments Denote That The Model Can Not Only Effectively Analyze User Group Behavior Regarding Rumor But Also Accurately Reflect The Competition And Symbiotic Relation Between Rumor And Anti-rumor Diffusion.

LIGHTWEIGHT THREE FACTOR AUTHENTICATION- Lightweight Three-Factor Authentication And Key Agreement Protocol For Internet-Integrated Wireless Sensor Networks

Wireless Sensor Networks (WSNs) Will Be Integrated Into The Future Internet As One Of The Components Of The Internet Of Things, And Will Become Globally Addressable By Any Entity Connected To The Internet. Despite The Great Potential Of This Integration, It Also Brings New Threats, Such As The Exposure Of Sensor Nodes To Attacks Originating From The Internet. In This Context, Lightweight Authentication And Key Agreement Protocols Must Be In Place To Enable End-to-end Secure Communication. Recently, Amin Et Al. Proposed A Three-factor Mutual Authentication Protocol For WSNs. However, We Identified Several Flaws In Their Protocol. We Found That Their Protocol Suffers From Smart Card Loss Attack Where The User Identity And Password Can Be Guessed Using Offline Brute Force Techniques. Moreover, The Protocol Suffers From Known Session-specific Temporary Information Attack, Which Leads To The Disclosure Of Session Keys In Other Sessions. Furthermore, The Protocol Is Vulnerable To Tracking Attack And Fails To Fulfill User Untraceability. To Address These Deficiencies, We Present A Lightweight And Secure User Authentication Protocol Based On The Rabin Cryptosystem, Which Has The Characteristic Of Computational Asymmetry. We Conduct A Formal Verification Of Our Proposed Protocol Using ProVerif In Order To Demonstrate That Our Scheme Fulfills The Required Security Properties. We Also Present A Comprehensive Heuristic Security Analysis To Show That Our Protocol Is Secure Against All The Possible Attacks And Provides The Desired Security Features. The Results We Obtained Show That Our New Protocol Is A Secure And Lightweight Solution For Authentication And Key Agreement For Internet-integrated WSNs.

GEOGRAPHIC AND OPPORTUNISTIC ROUTING- Geographic And Opportunistic Routing For Underwater Sensor Networks

Underwater Wireless Sensor Networks (UWSNs) Have Been Showed As A Promising Technology To Monitor And Explore The Oceans In Lieu Of Traditional Undersea Wireline Instruments. Nevertheless, The Data Gathering Of UWSNs Is Still Severely Limited Because Of The Acoustic Channel Communication Characteristics. One Way To Improve The Data Collection In UWSNs Is Through The Design Of Routing Protocols Considering The Unique Characteristics Of The Underwater Acoustic Communication And The Highly Dynamic Network Topology. In This Paper, We Propose The GEDAR Routing Protocol For UWSNs. GEDAR Is An Anycast, Geographic And Opportunistic Routing Protocol That Routes Data Packets From Sensor Nodes To Multiple Sonobuoys (sinks) At The Sea's Surface. When The Node Is In A Communication Void Region, GEDAR Switches To The Recovery Mode Procedure Which Is Based On Topology Control Through The Depth Adjustment Of The Void Nodes, Instead Of The Traditional Approaches Using Control Messages To Discover And Maintain Routing Paths Along Void Regions. Simulation Results Show That GEDAR Significantly Improves The Network Performance When Compared With The Baseline Solutions, Even In Hard And Difficult Mobile Scenarios Of Very Sparse And Very Dense Networks And For High Network Traffic Loads.

DATA STREAM - Publicly Verifiable Inner Product Evaluation Over Outsourced Data Streams Under Multiple Keys

Uploading Data Streams To A Resource-rich Cloud Server For Inner Product Evaluation, An Essential Building Block In Many Popular Stream Applications (e.g., Statistical Monitoring), Is Appealing To Many Companies And Individuals. On The Other Hand, Verifying The Result Of The Remote Computation Plays A Crucial Role In Addressing The Issue Of Trust. Since The Outsourced Data Collection Likely Comes From Multiple Data Sources, It Is Desired For The System To Be Able To Pinpoint The Originator Of Errors By Allotting Each Data Source A Unique Secret Key, Which Requires The Inner Product Verification To Be Performed Under Any Two Parties' Different Keys. However, The Present Solutions Either Depend On A Single Key Assumption Or Powerful Yet Practically-inefficient Fully Homomorphic Cryptosystems. In This Paper, We Focus On The More Challenging Multi-key Scenario Where Data Streams Are Uploaded By Multiple Data Sources With Distinct Keys. We First Present A Novel Homomorphic Verifiable Tag Technique To Publicly Verify The Outsourced Inner Product Computation On The Dynamic Data Streams, And Then Extend It To Support The Verification Of Matrix Product Computation. We Prove The Security Of Our Scheme In The Random Oracle Model. Moreover, The Experimental Result Also Shows The Practicability Of Our Design.

MULTITASK NETWORKS- Diffusion LMS Over Multitask Networks

The Diffusion LMS Algorithm Has Been Extensively Studied In Recent Years. This Efficient Strategy Allows To Address Distributed Optimization Problems Over Networks In The Case Where Nodes Have To Collaboratively Estimate A Single Parameter Vector. Nevertheless, There Are Several Problems In Practice That Are Multitask-oriented In The Sense That The Optimum Parameter Vector May Not Be The Same For Every Node. This Brings Up The Issue Of Studying The Performance Of The Diffusion LMS Algorithm When It Is Run, Either Intentionally Or Unintentionally, In A Multitask Environment. In This Paper, We Conduct A Theoretical Analysis On The Stochastic Behavior Of Diffusion LMS In The Case Where The Single-task Hypothesis Is Violated. We Analyze The Competing Factors That Influence The Performance Of Diffusion LMS In The Multitask Environment, And Which Allow The Algorithm To Continue To Deliver Performance Superior To Non-cooperative Strategies In Some Useful Circumstances. We Also Propose An Unsupervised Clustering Strategy That Allows Each Node To Select, Via Adaptive Adjustments Of Combination Weights, The Neighboring Nodes With Which It Can Collaborate To Estimate A Common Parameter Vector. Simulations Are Presented To Illustrate The Theoretical Results, And To Demonstrate The Efficiency Of The Proposed Clustering Strategy.

FILE TRANSFER USING CRYPTOGRAPHY TECHNIQUES

In This Project We Are Conducting The Investigation Studies Over The IT Auditing For Assuming The Security For Cloud Computing. During This Investigation, We Are Implementing Working Of IT Auditing Mechanism Over The Cloud Computing Framework In Order To Assure The Desire Level Of Security

MALWARE DETECTION- Behavioral Malware Detection In Delay Tolerant Networks

The Delay-tolerant-network (DTN) Model Is Becoming A Viable Communication Alternative To The Traditional Infrastructural Model For Modern Mobile Consumer Electronics Equipped With Short-range Communication Technologies Such As Bluetooth, NFC, And Wi-Fi Direct. Proximity Malware Is A Class Of Malware That Exploits The Opportunistic Contacts And Distributed Nature Of DTNs For Propagation. Behavioral Characterization Of Malware Is An Effective Alternative To Pattern Matching In Detecting Malware, Especially When Dealing With Polymorphic Or Obfuscated Malware. In This Paper, We First Propose A General Behavioral Characterization Of Proximity Malware Which Based On Naive Bayesian Model, Which Has Been Successfully Applied In Non-DTN Settings Such As Filtering Email Spams And Detecting Botnets. We Identify Two Unique Challenges For Extending Bayesian Malware Detection To DTNs ("insufficient Evidence Versus Evidence Collection Risk" And "filtering False Evidence Sequentially And Distributedly"), And Propose A Simple Yet Effective Method, Look Ahead, To Address The Challenges. Furthermore, We Propose Two Extensions To Look Ahead, Dogmatic Filtering, And Adaptive Look Ahead, To Address The Challenge Of "malicious Nodes Sharing False Evidence." Real Mobile Network Traces Are Used To Verify The Effectiveness Of The Proposed Methods.

SCALE FREE TOPOLOGIES - Constructing Limited Scale-Free Topologies Over Peer-To-Peer Networks

Overlay Network Topology Together With Peer/data Organization And Search Algorithm Are The Crucial Components Of Unstructured Peer-to-peer (P2P) Networks As They Directly Affect The Efficiency Of Search On Such Networks. Scale-free (powerlaw) Overlay Network Topologies Are Among Structures That Offer High Performance For These Networks. A Key Problem For These Topologies Is The Existence Of Hubs, Nodes With High Connectivity. Yet, The Peers In A Typical Unstructured P2P Network May Not Be Willing Or Able To Cope With Such High Connectivity And Its Associated Load. Therefore, Some Hard Cutoffs Are Often Imposed On The Number Of Edges That Each Peer Can Have, Restricting Feasible Overlays To Limited Or Truncated Scale-free Networks. In This Paper, We Analyze The Growth Of Such Limited Scale-free Networks And Propose Two Different Algorithms For Constructing Perfect Scale-free Overlay Network Topologies At Each Instance Of Such Growth. Our Algorithms Allow The User To Define The Desired Scalefree Exponent (gamma). They Also Induce Low Communication Overhead When Network Grows From One Size To Another. Using Extensive Simulations, We Demonstrate That These Algorithms Indeed Generate Perfect Scale Free Networks (at Each Step Of Network Growth) That Provide Better Search Efficiency In Various Search Algorithms Than The Networks Generated By The Existing Solutions.

ENHANCED INTERIOR GATEWAY ROUTING PROTOCOL - Performance Analysis Of Ospf And Eigrp Routing Protocols For Greener Internetworking

Routing Protocol Is Taking A Vital Role In The Modern Internet Era. A Routing Protocol Determines How The Routers Communicate With Each Other To Forward The Packets By Taking The Optimal Path To Travel From A Source Node To A Destination Node. In This Paper We Have Explored Two Eminent Protocols Namely, Enhanced Interior Gateway Routing Protocol (EIGRP) And Open Shortest Path First (OSPF) Protocols. Evaluation Of These Routing Protocols Is Performed Based On The Quantitative Metrics Such As Convergence Time, Jitter, End-to- End Delay, Throughput And Packet Loss Through The Simulated Network Models. The Evaluation Results Show That EIGRP Routing Protocol Provides A Better Performance Than OSPF Routing Protocol For Real Time Applications. Through Network Simulations We Have Proved That EIGRP Is More CPU Intensive Than OSPF And Hence Uses A Lot Of System Power. Therefore EIGRP Is A Greener Routing Protocol And Provides For Greener Internetworking.

HONEYPOTS FOR DETECTING CYBER ATTACK - Creation And Management Of Website Honeypots For Detecting Targeted Cyber Attacks

Message Authentication Is One Of The Most Effective Ways To Thwart Unauthorized And Corrupted Messages From Being Forwarded In Wireless Sensor Networks (WSNs). For This Reason, Many Message Authentication Schemes Have Been Developed, Based On Either Symmetric-key Cryptosystems Or Public-key Cryptosystems. Most Of Them, However, Have The Limitations Of High Computational And Communication Overhead In Addition To Lack Of Scalability And Resilience To Node Compromise Attacks. To Address These Issues, A Polynomial-based Scheme Was Recently Introduced. However, This Scheme And Its Extensions All Have The Weakness Of A Built-in Threshold Determined By The Degree Of The Polynomial: When The Number Of Messages Transmitted Is Larger Than This Threshold, The Adversary Can Fully Recover The Polynomial. In This Paper, We Propose A Scalable Authentication Scheme Based On Elliptic Curve Cryptography (ECC). While Enabling Intermediate Nodes Authentication, Our Proposed Scheme Allows Any Node To Transmit An Unlimited Number Of Messages Without Suffering The Threshold Problem. In Addition, Our Scheme Can Also Provide Message Source Privacy. Both Theoretical Analysis And Simulation Results Demonstrate That Our Proposed Scheme Is More Efficient Than The Polynomial-based Approach In Terms Of Computational And Communication Overhead Under Comparable Security Levels While Providing Message Source Privacy.

HOP BY HOP MESSAGE AUTHENTICATION - Hop-By-Hop Message Authentication And Source Privacy In Wireless Sensor Networks

Message Authentication Is One Of The Most Effective Ways To Thwart Unauthorized And Corrupted Messages From Being Forwarded In Wireless Sensor Networks (WSNs). For This Reason, Many Message Authentication Schemes Have Been Developed, Based On Either Symmetric-key Cryptosystems Or Public-key Cryptosystems. Most Of Them, However, Have The Limitations Of High Computational And Communication Overhead In Addition To Lack Of Scalability And Resilience To Node Compromise Attacks. To Address These Issues, A Polynomial-based Scheme Was Recently Introduced. However, This Scheme And Its Extensions All Have The Weakness Of A Built-in Threshold Determined By The Degree Of The Polynomial: When The Number Of Messages Transmitted Is Larger Than This Threshold, The Adversary Can Fully Recover The Polynomial. In This Paper, We Propose A Scalable Authentication Scheme Based On Elliptic Curve Cryptography (ECC). While Enabling Intermediate Nodes Authentication, Our Proposed Scheme Allows Any Node To Transmit An Unlimited Number Of Messages Without Suffering The Threshold Problem. In Addition, Our Scheme Can Also Provide Message Source Privacy. Both Theoretical Analysis And Simulation Results Demonstrate That Our Proposed Scheme Is More Efficient Than The Polynomial-based Approach In Terms Of Computational And Communication Overhead Under Comparable Security Levels While Providing Message Source Privacy.

SECURE DATA AGGREGATION - Secure Data Aggregation In Wireless Sensor Networks Filtering Out The Attackers Impact

Wireless Sensor Networks (WSNs) Are Increasingly Used In Many Applications, Such As Volcano And Fire Monitoring, Urban Sensing, And Perimeter Surveillance. In A Large WSN, In-network Data Aggregation (i.e., Combining Partial Results At Intermediate Nodes During Message Routing) Significantly Reduces The Amount Of Communication Overhead And Energy Consumption. The Research Community Proposed A Loss-resilient Aggregation Framework Called Synopsis Diffusion, Which Uses Duplicate-insensitive Algorithms On Top Of Multipath Routing Schemes To Accurately Compute Aggregates (e.g., Predicate Count Or Sum). However, This Aggregation Framework Does Not Address The Problem Of False Subaggregate Values Contributed By Compromised Nodes. This Attack May Cause Large Errors In The Aggregate Computed At The Base Station, Which Is The Root Node In The Aggregation Hierarchy. In This Paper, We Make The Synopsis Diffusion Approach Secure Against The Above Attack Launched By Compromised Nodes. In Particular, We Present An Algorithm To Enable The Base Station To Securely Compute Predicate Count Or Sum Even In The Presence Of Such An Attack. Our Attack-resilient Computation Algorithm Computes The True Aggregate By Filtering Out The Contributions Of Compromised Nodes In The Aggregation Hierarchy. Extensive Analysis And Simulation Study Show That Our Algorithm Outperforms Other Existing Approaches.

SECURE DATA RETRIEVAL - Secure Data Retrieval For Decentralized Disruption-Tolerant Military Networks

Mobile Nodes In Military Environments Such As A Battlefield Or A Hostile Region Are Likely To Suffer From Intermittent Network Connectivity And Frequent Partitions. Disruption-tolerant Network (DTN) Technologies Are Becoming Successful Solutions That Allow Wireless Devices Carried By Soldiers To Communicate With Each Other And Access The Confidential Information Or Command Reliably By Exploiting External Storage Nodes. Some Of The Most Challenging Issues In This Scenario Are The Enforcement Of Authorization Policies And The Policies Update For Secure Data Retrieval. Ciphertext-policy Attribute-based Encryption (CP-ABE) Is A Promising Cryptographic Solution To The Access Control Issues. However, The Problem Of Applying CP-ABE In Decentralized DTNs Introduces Several Security And Privacy Challenges With Regard To The Attribute Revocation, Key Escrow, And Coordination Of Attributes Issued From Different Authorities. In This Paper, We Propose A Secure Data Retrieval Scheme Using CP-ABE For Decentralized DTNs Where Multiple Key Authorities Manage Their Attributes Independently. We Demonstrate How To Apply The Proposed Mechanism To Securely And Efficiently Manage The Confidential Data Distributed In The Disruption-tolerant Military Network.

COLLABORATIVE PROTECTION- Firecol - A Collaborative Protection Network For The Detection Of Flooding Ddos Attacks

Distributed Denial-of-service (DDoS) Attacks Are A Major Threat To Security Issues. The Control And Resolving Of DDoS Attacks Is Difficult In A Distributed Network. The Primary Problem Till Date Is The Attacks Are Detected Close To The Victim And Hence Cannot Be Resolved. It Is Essential To Detect Them Early In Order To Protect Vulnerable Resources Or Potential Victims. FireCol Comprises Multiple Intrusion Prevention Systems (IPSs) Located At The Internet Service Providers (ISPs) Level. These Multiple Intrusion Prevention Systems (IPSs) Act As Traffic Filters. Based On Threshold Values It Passes Information. The Efficient System Of FireCol Is Demonstrated As A Scalable System With Low Overhead.

PROFILE MATCHING- Fully Anonymous Profile Matching In Mobile Social Networks

In This Paper, We Study User Profile Matching With Privacy-preservation In Mobile Social Networks (MSNs) And Introduce A Family Of Novel Profile Matching Protocols. We First Propose An Explicit Comparison-based Profile Matching Protocol (eCPM) Which Runs Between Two Parties, An Initiator And A Responder. The ECPM Enables The Initiator To Obtain The Comparison-based Matching Result About A Specified Attribute In Their Profiles, While Preventing Their Attribute Values From Disclosure. We Then Propose An Implicit Comparison-based Profile Matching Protocol (iCPM) Which Allows The Initiator To Directly Obtain Some Messages Instead Of The Comparison Result From The Responder. The Messages Unrelated To User Profile Can Be Divided Into Multiple Categories By The Responder. The Initiator Implicitly Chooses The Interested Category Which Is Unknown To The Responder. Two Messages In Each Category Are Prepared By The Responder, And Only One Message Can Be Obtained By The Initiator According To The Comparison Result On A Single Attribute. We Further Generalize The ICPM To An Implicit Predicate-based Profile Matching Protocol (iPPM) Which Allows Complex Comparison Criteria Spanning Multiple Attributes. The Anonymity Analysis Shows All These Protocols Achieve The Confidentiality Of User Profiles. In Addition, The ECPM Reveals The Comparison Result To The Initiator And Provides Only Conditional Anonymity; The ICPM And The IPPM Do Not Reveal The Result At All And Provide Full Anonymity. We Analyze The Communication Overhead And The Anonymity Strength Of The Protocols. We Then Present An Enhanced Version Of The ECPM, Called ECPM+, By Combining The ECPM With A Novel Prediction-based Adaptive Pseudonym Change Strategy. The Performance Of The ECPM And The ECPM+ Are Comparatively Studied Through Extensive Trace-based Simulations. Simulation Results Demonstrate That The ECPM+ Achieves Significantly Higher Anonymity Strength With Slightly Larger Number Of Pseudonyms Than The ECPM.

DISTRIBUTED SYSTEM- Optimal Client-Server Assignment For Internet Distributed Systems

We Investigate An Underlying Mathematical Model And Algorithms For Optimizing The Performance Of A Class Of Distributed Systems Over The Internet. Such A System Consists Of A Large Number Of Clients Who Communicate With Each Other Indirectly Via A Number Of Intermediate Servers. Optimizing The Overall Performance Of Such A System Then Can Be Formulated As A Client-server Assignment Problem Whose Aim Is To Assign The Clients To The Servers In Such A Way To Satisfy Some Prespecified Requirements On The Communication Cost And Load Balancing. We Show That 1) The Total Communication Load And Load Balancing Are Two Opposing Metrics, And Consequently, Their Tradeoff Is Inherent In This Class Of Distributed Systems; 2) In General, Finding The Optimal Client-server Assignment For Some Prespecified Requirements On The Total Load And Load Balancing Is NP-hard, And Therefore; 3) We Propose A Heuristic Via Relaxed Convex Optimization For Finding The Approximate Solution. Our Simulation Results Indicate That The Proposed Algorithm Produces Superior Performance Than Other Heuristics, Including The Popular Normalized Cuts Algorithm.

SPECTRUM AVAILABITIY- Spectrum-Availabitiy Based Routing For Cognitive Sensor Network

With The Occurrence Of Internet Of Things (IoT) Era, The Proliferation Of Sensors Coupled With The Increasing Usage Of Wireless Spectrums Especially The ISM Band Makes It Difficult To Deploy Real-life IoT. Currently, The Cognitive Radio Technology Enables Sensors Transmit Data Packets Over The Licensed Spectrum Bands As Well As The Free ISM Bands. The Dynamic Spectrum Access Technology Enables Secondary Users (SUs) Access Wireless Channel Bands That Are Originally Licensed To Primary Users. Due To The High Dynamic Of Spectrum Availability, It Is Challenging To Design An Efficient Routing Approach For SUs In Cognitive Sensor Networks. We Estimate The Spectrum Availability And Spectrum Quality From The View Of Both The Global Statistical Spectrum Usage And The Local Instant Spectrum Status, And Then Introduce Novel Routing Metrics To Consider The Estimation. In Our Novel Routing Metrics, One Retransmission Is Allowed To Restrict The Number Of Rerouting And Then Increase The Routing Performance. Then, The Related Two Routing Algorithms According To The Proposed Routing Metrics Are Designed. Finally, Our Routing Algorithms In Extensive Simulations Are Implemented To Evaluate The Routing Performance, And We Find That The Proposed Algorithms Achieve A Significant Performance Improvement Compared With The Reference Algorithm.

CLUSTERING VIEW POINT - Clustering With Multi-Viewpoint Based Similarity Measure

Clustering Is A Useful Technique That Organizes A Large Quantity Of Unordered Text Documents Into A Small Number Of Meaningful And Coherent Cluster, Thereby Providing A Basis For Intuitive And Informative Navigation And Browsing Mechanisms. There Are Some Clustering Methods Which Have To Assume Some Cluster Relationship Among The Data Objects That They Are Applied On. Similarity Between A Pair Of Objects Can Be Defined Either Explicitly Or Implicitly. The Major Difference Between A Traditional Dissimilarity/similarity Measure And Ours Is That The Former Uses Only A Only A Single Viewpoint, Which Is The Origin, While The Latter Utilizes Many Different Viewpoints, Which Are Objects Assumed To Not Be In The Same Cluster With The Two Objects Being Measured. Using Multiple Viewpoints, More Informative Assessment Of Similarity Could Be Achieved. Theoretical Analysis And Empirical Study Are Conducted To Support This Claim. Two Criterion Functions For Document Clustering Are Proposed Based On This New Measure. We Compare Them With Several Well-known Clustering Algorithms That Use Other Popular Similarity Measures On Various Document Collections To Verify The Advantages Of Our Proposal