Mutual Exclusion in Wireless Sensor and Actor Networks IEEE SECON 2006 Ramanuja Vedantham, Zhenyun Zhuang and Raghupathy Sivakumar Presented by Jang Chol Soon

-2- Contents Introduction Problem Definition Context Different types of Mutual Exclusion Challenges & Goals Centralized Approach Distributed Approach Performance Evaluation Conclusions

-3- Introduction Wireless Sensor Networks (WSNs) One type of action: ‘sensing’ the environment Performance evaluation: read operations Wireless Sensor and Actor Networks (WSANs) Two types of action: ‘sensing’ and ‘acting’ on the environment Performance evaluation: read and write operations

-4- Introduction e.g. Automated sprinkler system in WSAN Sensors (humidity sensors) Actors (sprinklers) A minimum subset of sprinklers is activated to cover the entire region Overall sprinkler resources (water) and energy is minimized.

-5- Introduction The outcome of not acting to the appropriate level depending on the nature of the application in WSANs Inefficient usage of actor resources Incorrect operation A catastrophic situation Mutual Exclusion : providing mutually exclusive acting regions to cover an event region

-6- Introduction Mutual Exclusion Algorithms It used in concurrent programming to avoid the simultaneous use of a common resource, such as a global variable, by piece of computer code called critical sections.

-7- Introduction The challenges to provide Mutual Exclusion How do we provide mutual exclusion, when there are events of varying intensities? Is the approach generic to address different types of events such as point/multi-point events as well as regional events? What happens when the event area decreases or increases? Ⅰ. A greedy centralized approach Ⅱ. A localized and fully distributed approach

-8- Problem Definition A. Context Architectural model sink - serves as the coordination entity. - issues directives to both sensors and actors. sensor actor The problem of mutual exclusion in the context of WSANs - Given a set of actors in an event region, what is the minimum subset of actors that covers the entire event region such that there is minimal overlap in the acting regions? sink sensoractor

-9- Problem Definition Notations to Define Types of Mutual Exclusion

-10- Problem Definition Different Regions based on the Notation

-11- Problem Definition B. The different types of Mutual Exclusion in WSANs Resource Critical Mutual Exclusion Overlap-type Critical Mutual Exclusion Overlap-Area Critical Mutual Exclusion Overlap-Intensity Critical Mutual Exclusion ※ Context : regional events requiring only one round of execution with no event dynamics

-12- Problem Definition Resource Critical Mutual Exclusion To maximize the non-overlapped acting regions of each actor within the event region in order to utilize the actor resources to the least extent. The minimal overlap in acting regions. Definition - To determine the minimum set of actors, M - Maximizes the overall benefit function by the sum of individual benefit function e.g. A fire extinguisher application

-13- Problem Definition Overlap-Type Critical Mutual Exclusion When there is a threshold for the desired level of action and any amount of action beyond this threshold is perceived as undesirable. Definition - To find the minimum set of actors, M - To maximize the overall benefit function defined by the sum of individual benefit function - α is a constant that represents the cost incurred in having new overlaps in the event region e.g. An intruder-detection and automated-tranquilizer application

-14- Problem Definition Overlap-Area Critical Mutual Exclusion To maximize the amount of non-overlapped region covered by each actor To minimize the amount of overlapping regions (both old and new) Definition - To determine the minimum set of actors, M - Maximizes the non-overlapping and minimizes the total overlapping regions of the actor cover - β is a constant that represents the cost incurred in having any kind of overlap in the event region e.g. A fire extinguisher application

-15- Problem Definition Overlap-Intensity Critical Mutual Exclusion Every overlap beyond a threshold is deemed as undesirable, and the weight of the function depends on the number of times the overlap occurs for a particular region (intensity of overlap) Definition - To determine the minimum set of actors, M - Maximizes the non-overlapping and minimizes the total overlapping regions based on the intensity - is the weighting factor that represents the cost incurred in having an overlap with intensity in the event region e.g. A fire extinguisher application

-16- Problem Definition C. Challenges for other types of applications Differing Event Intensity: (a) Point/Multi-point Events: (b) Event Dynamics: (c) (d)

-17- Problem Definition D. Goals Overheads - small Correctness - the percentage of area covered by the actor cover set in comparison with the total event region - is able to cover the entire even region

-18- Centralized Approach Assumptions Network Model - sensors and actors : static, randomly distributed Location Information - localization algorithms Sensing, Acting and Communication Ranges - same Routing Model - an underlying reliable routing protocol

-19- Centralized Approach A greedy, centralized algorithm To alleviate the mutual exclusion problem Actor’s selection criteria : benefit function of actors Mechanism - selecting and adding the actor with the maximum benefit function at each stage - benefit function : defined by the type of mutual exclusion - terminates when the selected set of actors cover the complete event region Optimality of the approach - NP-hard (Nondeterministic Polynomial-time hard) - The upper bound of the competitive ratio:

-20- Centralized Approach Mechanism M - The set of actors selected as part of actor cover at any given stage - Initially, an empty set MAX_ACTOR - The actor that has the maximum benefit function MAX_BENEFIT - The non-overlapped region of this actor

-21- Distributed Approach Distributed and fully localized approach Neighborhood Back-off (NB) approach addresses the challenges for other types of applications A distributed realization of the centralized strategy Automatic updates to benefit functions of all entities within each dependency region Dependency region for a sensor or an actor (entity) - The maximum region with which another entity can have an impact on its execution range - The dependency region of a sensor : Sensing Range + Acting Range - The dependency region of a actor : 2 * Acting Range

-22- Distributed Approach Distributed and fully localized approach Basic operations after determining of the dependency region - The determination of initial benefic function for each actor : issued by the sensor to the actors in its dependency region - The emulation of the greedy centralized strategy at each actor by waiting time for an amount of time : Benefit function ↑, waiting time ↓ Benefit function ↓, waiting time ↑ - The updating of the benefit functions for all actor within the dependency region of an actor

-23- Distributed Approach The Neighborhood Back-off Approach Construction of Dependency Regions - The initial set up of the network - One-time discovery process to determine the set of actors within the dependency region of a sensor or an actor Operations at the Sensors - reports the sensed information to the sink and receives the command directive from the sink - every sensor in the event region constructs a shortest path tree within its dependency region - sending REQUEST() or CANCEL() directives to all the actors in its dependency region

-24- Distributed Approach The Neighborhood Back-off Approach Operations at the Actors - determines the event region in its acting range base on the REQUEST() directive received from the sensors - every actor in the event region constructs a shortest path tree within its dependency region - receives a REQUEST() directive from a sensor - determines the additional event area covered by the sensor and add that region to already existing event area (virtual metric : used to determine the wait time for a actor) - NOTIFY() transmission, Flag(), Transmit(), wait()

event -25- Distributed Approach sink sensor actor sensed info command directive REQUEST(Dir_id, Xs i, Ys i ) IF wait time <= 0 send NOTIFY() IF Flag() checked Transmit() ELSE wait() ELSE wait() update benefit function

-26- Distributed Approach Mechanism

-27- Distributed Approach Mechanism for Addressing Challenges Handling Varying Event Intensities - adapting the actor cover algorithm based on the difference in the intensity across the event region Handling Point Events - Selecting a minimum set of actors that covers all the point events without any overlap Handling Event Dynamics - The increasing in the event area : REQUEST() - The decreasing in the even area : CANCEL()

-28- Performance Evaluation Performance evaluation for three approaches Centralized Set Cover (CSC) : 100% correctness Minimum Dominating Set (MDS) : 70% correctness Neighborhood Back-off (NB) : 100% correctness Context for Performance evaluation The benefit function : Resource Critical Mutual Exclusion A custom built, event-driven simulator written in C sensors and 2000 actors are randomly placed on a 3000m * 3000m square area - The sensing and communication range of sensors : 30m - The default event radius : 100m ( ~ 500m) - The default distance from the event center to the sink : 1500m ( 500 ~ 2500m) - Bounded delay : 10 sec- packet size : 1KB

-29- Performance Evaluation Varying the Event Area Size NB approach : the best performance in terms of communication cost

-30- Performance Evaluation Varying the Event Area Size NB approach : the worst performance in terms of overlapped action areas and Number of actors, but 100% correctness

-31- Performance Evaluation Varying the Distance from the Sink to the Event Center NB approach : no increase with increasing Sink-to-event distance due to the localized operation

-32- Performance Evaluation Varying the Delay Bound NB approach : execute the command at different times due to the back-off mechanism

-33- Performance Evaluation Varying the Delay Bound NB approach : little effect CSC and MDS approach : large effect

-34- Performance Evaluation Varying the Density of Actors - increase from 1 * 2000 actors to 3 * 2000 actors NB and CSC approach : incur a slightly larger communication overhead ( ※ NB performs slightly worse due to distributed operations) MDS approach : almost constant

-35- Performance Evaluation Varying the Density of Sensors - increase from 1 * 2000 actors to 3 * 2000 actors All three approaches : have a slightly larger overhead due to the marginal increase in communication cost to report sensor data

-36- Conclusions The problem of mutual exclusion in the context of WSANs - Generic different types of Mutual exclusion 1) Resource Critical Mutual Exclusion 2) Overlap-Type Critical Mutual Exclusion 3) Overlap-Area Critical Mutual Exclusion 4) Overlap-Intensity Critical Mutual Exclusion - Challenges 1) Differing Event Intensity 2) Point/Multi-point Events 3) Event Dynamics The solution to address the problem of mutual exclusion - A greedy centralized approach - A localized and fully distributed approach (NB approach)

-37- Discussion No consideration about Simultaneous occurrence of the analyzed problems about mutual exclusion Dedicated specification for assumptions in these approaches Reality Computation of Dependency region of a sensor or an actor

Q & A -38-